Market Coupling Research Articles

Effectiveness of CO2 cost pass-through to electricity prices under “electricity-carbon” market coupling in China

This paper analyzed the coupling relationship between the carbon market and power market and constructed a threshold regression model of carbon cost pass-through to electricity prices. Furthermore, a system dynamics model was built based on “electricity-carbon” market. The results show that first, the carbon market and power market operate independently but are interrelated. The two markets will work when Chinese emission allowances and carbon prices are effectively connected with carbon emissions and electricity prices. Second, because the electricity price formation mechanism is mainly regulated by China's government, the carbon price has a significant negative effect on the electricity price since the operation of the national carbon trading system. There is no obvious linkage effect between the CO2 cost and electricity price. Third, under the current policies and market mechanism, the carbon price will experience the process of “remain low-rapid rise-remain high”. The carbon price will drive the on-grid price and sales price up. The allowance auction mechanism and carbon cost direct pass-through can effectively reflect CO2 cost in the electricity price. In the long term, introducing auctioning in the carbon market and reforming electricity price formation in the power sector will tap the emission reduction potential.

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.

Research on spillover effect between carbon market and electricity market: Evidence from Northern Europe

Due to the consistency of participants and emission reduction targets, the carbon market and electricity market have the necessity and possibility of coordinated development. As matter stands, the information transmission and linkage between the two markets are still unclear and difficult to quantify. Thus, this paper innovatively conducts an in-depth study on the spillover effect between the carbon market and the electricity market, and further analyzes the intermediary role of the energy market in the carbon-electricity synergy. First, the return spillover effect and volatility spillover effect in various markets are researched based on the vector autoregressive (VAR), Baba–Engle–Kraft–Kroner (BEKK) and generalized autoregressive conditional heteroskedasticity (GARCH) model. Then, the relationship between oil, natural gas, carbon and electricity markets is further analyzed. Finally, an empirical study is carried out with the data of the Nordic electricity market and the European Union Emission Trading System. The result shows that the impact between the carbon market and the electricity market is more the transmission of price fluctuations than the direct impact of returns. Moreover, the energy markets play a “bridge” role in promoting carbon-electricity market coupling. Hence, it is necessary to further strengthen the prevention of external risks and improve the regulatory policy system, so as to ensure the smooth and sustainable operation of the market.

Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments

• This paper aims to enhance the modeling of cross-border energy exchanges in adequacy study. • Novel supervised learning-based models in single- and two-step set-ups are proposed. • Accuracy, scalability, and computational complexity of adequacy study are improved. • It can have high implication for policy makers and be of a great interest of academics. To represent the cross-border exchange capacities defined by the flow-based approach in the European resource adequacy assessments, transmission system operators currently employ a data-driven methodology that consists of sequential clustering and correlation steps. This methodology entails assumptions and simplifications within both clustering and correlation analyses that may lead to an erroneous representation of import–export capacities in the subsequent adequacy assessments. While the first stage of this methodology can be improved by leveraging a clustering technique tailored to adequacy assessments, the correlation step presents a poor performance in terms of accuracy and scalability. To address the latter challenges, this paper proposes a supervised learning-based model that can enhance the mapping between several relevant explanatory variables and the pre-clustered flow-based domains, leading to a more accurate representation of the flow-based domains in adequacy assessments. Furthermore, the current paper leverages supervised learning to develop a single-step approach that directly maps the selected explanatory variables to the flow-based domains using the K-Nearest Neighbors algorithm, eliminating the clustering step. This circumvents inaccuracies introduced by the significant intra-cluster discrepancies due to numerous shapes and forms of the flow-based domains and enables an enhanced modeling of the flow-based domains in adequacy assessments. In an extensive case study, we demonstrate that the proposed single-step model can significantly improve the accuracy of adequacy assessments, compared to the best-in-class result obtained by the two-step set-up. Moreover, the proposed single-step model involves no hyper-parameters, eliminates the computational complexity of the two-step set-up, and efficiently upscales to integrate the new zones joining to the flow-based market coupling.

Multi-Market Bidding Behavior Analysis of Energy Storage System Based on Inverse Reinforcement Learning

The bidding behaviors of the energy storage systems (ESS) are complicated due to time coupling and market coupling limited by their capacity states. The existing research is mainly based on optimization models and reinforcement learning (RL) models, which are idealized with analytical objective functions, rational decisions, and virtual historical data. This leads to mismatches between theoretical results and actual bidding behaviors, and the obtained results cannot be generalized to a real-world market. In recent years, the disclosure of market data has enabled data-driven analysis, while the complexity of actual data makes the analysis process challenging and calls for more suitable methods. On the basis of our investigation of ESS bidding behaviors and market data, we propose a novel inverse RL (IRL)-based framework to identify the bidding decision objective function of ESS in coupled multi-market through their historical bidding records and operation status. The identification results can be used as the model parameters to help RL-based simulation models available for a real-world market. Based on the proposed framework, we conduct an empirical analysis on the real-world data of a battery ESS in the Australian electricity market. The results show how the ESS balances its objective among different markets and maintains its state of charge (SOC) to gain more profits. This framework can help us model ESS behaviors by relying on historical data to better understand the ESS bidding decision-making mechanism, which will facilitate market equilibrium analysis and mechanism design.

Coordinating Capacity Calculation via Electricity Market Coupling: Insights from the H2020 CROSSBOW Project

CROSS BOrder management of variable renewable energies and storage units enabling a transnational Wholesale market (CROSSBOW) is an EC-funded project, whose aim is to facilitate the shared use of energy resources by fostering cross-border management of variable renewable energies and storage units, enabling higher penetration of renewable energy sources (RES) whilst reducing network operational costs and improving economic benefits of clean energies and storage units. Towards these goals, CROSSBOW boosts regional cooperation among the system operators in Southeastern Europe (SEE), by deploying nine different tools to support the security coordination center (SCC) of the region. More specifically, the main CROSSBOW product, namely CROSSBOW Regional Operation Centre (CROSSBOW ROC) has proposed and demonstrated a set of functionalities for regional management and operation that enhance the existing regional structures, extending the capabilities of the already established Regional Security Coordinator (RSC) initiatives. Beyond enhancing RSC mandatory functions (including adequacy forecasts, coordinated security analysis, capacity calculations, and outage planning coordination), the ROC-BC product has developed new functions, linking the security considerations of involved TSOs with the operation of the fast-developing and harmonized electricity markets. In this paper, we investigate approaches for coordinated capacity calculation and cross-border trading via market coupling, developed within the ROC-BC product of CROSSBOW. Moreover, we present the final demonstration results as a part of ROC fundamental functionalities. Specifically, both net transfer capacity (NTC) and flow-based (FB) methods are examined and compared within a case study applying to the SEE region. The presented results demonstrate that the FB method exhibits better performance in all examined scenarios, considering three different key performance indicators (KPIs).

The capacity allocation method of European electricity market and its implications for China

Nowadays, the transaction scale of the cross-provincial electricity market in China has increased, and the problem of transmission capacity allocation attracts the government’s attention. During the development of the European unified electricity market, capacity allocation methods based on bidding zones such as Available Transfer Capacity (ATC) and Flow-Based Market Coupling (FBMC) have realized regional market coupling and improved grid utilization. This article intends to analyze the allocation methods of transmission capacity in the European electricity market, focusing on the comparison between the ATC and the FMBC, considering the actual background of the Chinese electricity market, especially the differences between the Chinese market and the European market in the grid structure and marketization process, it is more suitable to adopt ATC in China and it makes the suggestion for the application of the capacity allocation method. Besides, the idea of bidding zone simplification is also worth learning. It is also recommended that provincial institutions exchange data related to capacity allocation to coordinate the calculation of trading domains.

An analysis of zonal electricity pricing from a long-term perspective

In an era of energy transition, it is crucial to ensure that the design of the short-term electricity market provides sufficient cash flows to producers so as to allow the investment of the right technology at the right location. In this paper, we revisit the question of capacity allocation in zonal markets from a long-term perspective. We model the capacity expansion problem in zonal markets in which inter-zonal transmission capacity allocation is organized through flow-based market coupling, which is an approximation of power flow equations in aggregate networks that is employed in European market design. We demonstrate that the classical result of equivalence between centralized and decentralized formulations in transmission-constrained markets ceases to hold in this case. We propose a model of the decentralized capacity expansion problem with flow-based market coupling as a generalized Nash equilibrium that we formulate as a linear complementarity problem. We then perform simulations of the capacity expansion problem with nodal pricing and three variations of zonal pricing on a realistic instance of the Central Western European network and comment on the impacts of flow-based market coupling on investment.

Electricity market integration and impact of renewable energy sources in the Central Western Europe region: Evolution since the implementation of the Flow-Based Market Coupling mechanism

The wholesale electricity markets in Europe are undergoing major changes. The pursuit of a major integration for the development of the Internal Energy market is the main driver of this transformation. In this context, the Central Western Europe region implemented for the first time in May 2015 a new and complex mechanism to manage cross-border capacity allocation in the frame of the day-ahead market: the Flow-Based Market Coupling. This paper is the first to consider recent data of the electricity market to develop a predictive model for identifying convergence and congestion situations in the Central Western Europe region. The occurrence of convergence or not is considered as a binary outcome to build a probit model. This model combines meaningful information about the electricity market features of the interconnected countries between 2016 and 2018, including for the first time data on renewable energy sources forecasts for this type of studies. The results of this study identify the major role that Germany and France play in the integration process. The estimated coefficients of the model show that the strong development of solar and wind power in Germany appears as an important driver of congestion in the Central Western Europe. In order to improve the benefits of market integration and the further development of the Internal Energy market, the results of this study encourage policy-makers to promote cooperation and coordination among all the actors involved in the electricity market and to pursue the expansion of the power grid to minimize the occurrence of congestion situations.

On the Economic Impacts of European Balancing Market Integration: The Case of the Newly-Installed Afrr Market Coupling Platform

On the Economic Impacts of European Balancing Market Integration: The Case of the Newly-Installed Afrr Market Coupling Platform

The Effect of Flow-Based Market Coupling on Cross-Border Exchange Volumes and Price Convergence in Central-Western European Electricity Markets

The Effect of Flow-Based Market Coupling on Cross-Border Exchange Volumes and Price Convergence in Central-Western European Electricity Markets

Business Models of Social Enterprises: Insight into Key Components and Value Creation

The increasing pressure on people to do something for society, in combination with the need for financial turnover in order to survive, is seen as one of the dominant factors for the rise of social enterprises. However, there is still debate on how social enterprises create social value in addition to economic value and how this is reflected in the business model. In this case study research, we investigate how the key components of the business model of social enterprises contribute to the creation of social and economic value. The cases in this research create social and economic value through the mutually interacting operation of key components and their sub-components. This interacting effect focuses on the one hand on the alignment of the internal architecture, market and financial management with the mission. The mission statement serves as a guiding principle. Furthermore, realizing the highest possible profit is not a goal in itself for social entrepreneurs. Generating profit serves the continuity of the company and the realization of social value. We also found that social enterprises can be configured as either market hybrids, blending hybrids, bridging hybrids or coupling hybrids.

A Review on the Complementarity Modelling in Competitive Electricity Markets

In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications.

Improved selection of critical network elements for flow-based market coupling based on congestion patterns

European electricity markets are zonal markets, a set-up that naturally entails an imperfect representation of intra-zonal congestion patterns that may limit cross-border trade. The method of flow-based market coupling aims to reflect limitations to cross-border trade by incorporating intra- and interzonal grid elements within the setting of zonal pricing through monitoring the flows on these critical network elements caused by inter-zonal trade. A major challenge for grid operators is the selection of critical network elements, essentially deciding which grid elements send congestion signals and trade limitations to the markets. Our main research question is: Can insights on hypothetically re-configured market zones help to improve the selection of critical network elements and lead to cost reductions without effectively changing the market zone setting? Using a flow-based market coupling optimization model based on a 3-zone test network, we propose a hypothetical nodal price-based market zone re-configuration to identify congestion signals and derive an improved set of critical network elements. We find that around 90% of the cost reductions from this market zone re-configuration can be maintained when the critical network elements, obtained from the re-configured market zones, are used in the original 3-zone setting. This is a strong indication that, both in reality as well as model-based research of flow-based market coupling, the selection of critical network elements should be based on expected congestion patterns. The proposed approach can constitute a helpful addition to static and assumption-based selection criteria for critical network elements that are currently used by European grid operators.

Evolution of Fundamental Price Determination within Electricity Market Simulations

Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of market coupling and price determination. In this paper, we analyze a new single-stage approach that combines both steps and theoretically and empirically demonstrate its precision by a backtest. For this purpose, we compare a simulated versus a historical electricity price distribution. Moreover, we explain the necessary adjustments for future regulatory developments of the European electricity market regarding flow-based market coupling and propose a concept for the application of future regulatory developments. We demonstrate further developments using a future scenario.

Enhanced integration of flow-based market coupling in short-term adequacy assessment

The resource adequacy of the interconnected Central Western Europe (CWE) electricity system is assessed considering the cross-border exchange capacities defined through the Flow-Based (FB) domains. Integration of FB domains into adequacy assessments poses several challenges since the FB domains depend on factors which are not known over the horizon of adequacy study. Computing hourly FB domains for each generated scenario of adequacy study, firstly, requires adopting assumptions on those unknown parameters (that may not fully match with the reality). Secondly, it noticeably increases the computational complexity of the study. The above challenges can, however, be circumvented by the data-driven alternatives. This paper presents a novel clustering technique for FB domains, which is specifically tailored for adequacy assessments. In contrast to the classical approach employed by the CWE Transmission System Operators (TSOs), which clusters the FB domains based on their overall geometrical resemblance, the proposed technique relies on the maximum and minimum zonal balances allowed by the FB domains, which are decisive factors in the CWE resource adequacy assessments. Indeed, during scarcity moments, the zonal net positions (balances) tend to reach their extreme values to reduce the costs of energy not served. The proposed goal-oriented clustering technique is examined against the classical clustering methodology employed by the CWE TSOs. The conducted simulations demonstrate that the proposed technique considerably (by a factor of over 5.5) improves the accuracy of the CWE adequacy assessments while being scalable with the future evolution of the Flow-based Market Coupling (FBMC). As such, it has direct implications for the adequacy assessment considering the FB domains.

Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires the consideration of features from ever-growing coupling markets. This study provides a method of exploring the influence of market coupling on electricity price prediction. We apply state-of-the-art long short-term memory (LSTM) deep neural networks combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. LSTM models have a good performance in handling nonlinear and complex problems and processing time series data. In our empirical study of the Nordic market, the proposed models obtain considerably accurate results. The results show that feature selection is essential to achieving accurate prediction, and features from integrated markets have an impact on prediction. The feature importance analysis implies that the German market has a salient role in the price generation of Nord Pool.

Asian stock market integration after the global financial crisis: an ARDL bound testing approach

PurposeThe Global recession of 2008 was the worst financial crisis in the postworld war economic history that brought in severe disruptions in global investments and capital flows. Not surprisingly, research interest in the field of market integration has considerably increased over the last decade. This paper analyses the dynamics of price integration among Asian financial markets during the postfinancial crisis period.Design/methodology/approachWe employ an Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and a Granger Causality/Block Exogeniety test from a Vector Error Correction Model (VECM) on monthly stock index data of five leading Asian economies from April 2009 to March 2020.FindingsThe cointegration results could not produce any conclusive evidence of long-run relations between stock markets. There exists weak price convergence among markets, and financial integration is partial and in an imperfect form.Research limitations/implicationsStock price performance in China is closely “coupled” with that in India, but both markets appear to be the short-run predictors of Asian stock returns. The research uses only the benchmark stock indices of the selected economies. Consideration of mid-cap and small-cap segments where foreign investments are significant today can validate the findings further.Practical implicationsThe asymmetric pattern of price behavior of Asian markets has important implications for the pricing efficiency of national markets and offers arbitrage potentials for global investors to optimize returns through market diversifications on a long-term perspective. The finding definitely will be a great help to investors who are potentially interested in a trading strategy that offers greater returns with limited exposure to market risks.Originality/valueCompared with previous studies, the research uses the most recent data of leading Asian markets and applies the robust method of ARDL Bounds testing approach that allows us to understand better if the economic recoveries and advancement have had an effect on market coupling and stock price transmissions.

Impact of Flow Based Market Coupling on the European Electricity Markets

Flow Based Market Coupling is the target model for determining exchange capacities in the internal European Electricity Market. It has been in operation in Central Western Europe since 2015 and is scheduled to be extended to the wider Core region in the near future. Exchange capacities have a significant impact on market prices, exchanges and the energy mix, thus also determining the CO{}_{2} footprint of electricity generation in the system. Stakeholders therefore need to develop an understanding for the impact of Flow Based Market Coupling and the parameter choice, like the minimum exchange capacities introduced in 2020, on the market outcome. This article presents a framework to model Flow Based Market Coupling and analyse the impact of different levels of regulatory induced minimum trading capacities as well as the effect of the extension towards the Core region. Electricity prices, exchange positions and the number and nature of binding constraints in the market results under different market coupling scenarios are investigated. The results show that increased level of minimum trading capacities in CWE market coupling decrease the German net export position by up to 7 TW h or 23%, while French exports increase by up to 10 TW h or 9%. The different transfer capacity in the scenarios induce a price difference of up to 13%. Increased exchange capacities allow for more base load generation with the corresponding effects for the CO{}_{2} emissions of the system. The nature of coupling constraints is highly dynamic and dependent on the system state, which makes the suitability of static NTC values in energy system scenarios questionable.

Toward a fundamental understanding of flow-based market coupling for cross-border electricity trading

• Provision of a guide to the main steps and parameters of flow-based market coupling. • Learning goals to help readers understand actions by transmission system operators. • Provision of an open-access model (code and data) based on free-to-use software. • Case studies and sensitivity analyses based on a modified 118-bus IEEE network. • Quantifying effects of generation shift keys, critical lines and security margins. Trading electricity across market zones furthers competitive power prices, security of supply and the integration of renewable energy. In the European Union, flow-based market coupling is the target model to compute correct trading capacities between markets, while approximating physical grid constraints. The methodology relies on predictive, carefully designed parameters to maximize trading opportunities, while keeping congestion management at acceptable levels. This paper explains the fundamentals of flow-based market coupling. It provides an open-access model based on a test network and its data. By providing a guide to the theory and conducting several case studies, the functioning and effects of the most influential parameters are demonstrated. Innovative aspects of this paper are its illustrative nature and its answer to the following research questions: (1) What is the effect of (a) generation shift keys and (b) threshold choice on the selection of critical network elements? (2) What is the effect of (a) generation shift keys, (b) selected critical network elements and (c) security margins on market coupling and congestion management results? It also addresses the effect of higher shares of renewable energy and minimum trading capacities on flow-based market coupling. This analysis shows that (1) the effects of flow reliability margins and selected critical network elements dominate the effect of generation shift keys on flow-based market coupling domains, (2) power systems with an increasing share of renewable energy may necessitate re-adjusted parameters and (3) minimum trading capacities can be guaranteed by redispatch or altering the base case.