Efficient use of transmission capacity for cross-border trading: Available Transfer Capacity versus flow-based approach

Plancke, Glenn; Vos, Kristof De; Jonghe, Cedric De; Belmans, Ronnie

doi:10.1109/energycon.2016.7513974

Cited by 10 publications

(10 citation statements)

References 6 publications

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“…Note that this aspect distinguishes our illustrations from common TSO-style depiction (e.g., in[Plancke et al 2016]), which depict subspaces of the zonal FR in terms of bilateral exchanges.…”

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confidence: 99%

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

2019

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractBy interlinking power systems, significant welfare gains can be achieved. However, different approaches for coupling electricity markets exist. A recent development in this field is the implementation of flow-based market coupling (FBMC) in Central Western Europe (CWE). Indeed, FBMC has proven to be advantageous compared to the former situation in CWE Amprion et al. 2014 and to be operationally manageable under current market conditions. However, analyses of possible future adjustments of market areas ("bidding zones") have shown problems in assessing and even understanding FBMC Entso-E 2018. Therefore, Part I of this two-part paper contributes to resolving these problems. First, it presents key issues and effects of all essential FBMC elements. This is done drawing the feasible regions of the FBMC constraints -a method that is well-known from optimization theory. However, in the way it is applied to FBMC it is novel and offers significant insights. These insights are presented on the basis of a stylized yet fully transparent and reproducible example. Thereby, we improve the understanding of benefits and shortcomings of FBMC. Second, we introduce a large-scale model framework capable of assessing FBMC as implemented in CWE. This enables us to assess real-world power systemsalso when undergoing structural changes. This lays the foundation for the case study presented in Part II.

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confidence: 99%

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

2019

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“…Author in [22] simulated both zonal pricing methods, using 12-month data from the CWE market, and finds that FBMC improves social welfare by between e50M and e100M. The studies in [23] and [24] confirm these findings. However, these studies ignore distortions due to imperfect competition.…”

Section: Introductionmentioning

confidence: 84%

Production Efficiency of Nodal and Zonal Pricing in Imperfectly Competitive Electricity Markets

Sarfati¹,

Hesamzadeh

Holmberg

2019

SSRN Journal

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Electricity markets employ different congestion management methods to handle the limited transmission capacity of the power system. This paper compares production efficiency and other aspects of nodal and zonal pricing. We consider two types of zonal pricing: zonal pricing with Available Transmission Capacity (ATC) and zonal pricing with Flow-Based Market Coupling (FBMC).We develop a mathematical model to study the imperfect competition under zonal pricing with FBMC. Zonal pricing with FBMC is employed in two stages, a day-ahead market stage and a re-dispatch stage. We show that the optimality conditions and market clearing conditions can be reformulated as a mixed integer linear program (MILP), which is straightforward to implement. Zonal pricing with ATC and nodal pricing is used as our benchmarks. The imperfect competition under zonal pricing with ATC and nodal pricing are also formulated as MILP models. All MILP models are demonstrated on 6-node and the modified IEEE 24-node systems. Our numerical results show that the zonal pricing with ATC results in large production inefficiencies due to the incdec-game. Improving the representation of the transmission network as in the zonal pricing with FBMC mitigates the inc-dec game. Cambridge Working Papers in Economics Faculty of Economics www.eprg.group.cam.ac.uk

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“…The FBMC flow domain represents all feasible combinations of commercial exchanges between participating bidding zones. To calculate it, TSOs assess a number of quantities that are named below (Belgian Federal Commission for Electricity and Gas Regulation 2017; Plancke et al 2016). Figure 1 summarizes the flow-based capacity calculation on the basis of these quantities.…”

Section: Flow-based Market Couplingmentioning

confidence: 99%

“…Most studies in the literature focus on assessing the efficiency of FBMC for the market integration of power transfers between countries or price areas, in particular in comparison with the ATC method. For instance in Bjørndal et al (2018); Plancke et al (2016), different challenges of the FMBC model implementation are discussed, while (Bjorndal et al 2018) study the influence of the bidding zone configuration on the performance of both the ATC and the FMBC model. Another focus of current research are modified methodologies and sensitivities for the determination of flow-based parameters in the FBMC model, e. g. by using more efficient algorithms (Matthes et al 2017), parameter variations (Wyrwoll et al 2018) or a statistical approach to Generation Shift Keys (GSK's) (Schönheit and Sikora 2018).…”

Section: Related Workmentioning

confidence: 99%

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

et al. 2019

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Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.

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Efficient use of transmission capacity for cross-border trading: Available Transfer Capacity versus flow-based approach

Cited by 10 publications

References 6 publications

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

Production Efficiency of Nodal and Zonal Pricing in Imperfectly Competitive Electricity Markets

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

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