Impact of Flow Based Market Coupling on the European Electricity Markets

Abstract

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$${}_{2}$$ 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$${}_{2}$$ 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.

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

“…In the FB approach, the physical network constraints are considered [37][38][39]. That means that the capacity allocation is realized partially, ex ante the market clearing and partly at the same time with the market clearing [18].…”

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

“…Equation ( 9) implies that the generation in each zone must equal the sum of load demand and net power exchanges of that zone. To guarantee the feasibility of the problem at each studied time step, a slack variable i.e.,ENS i is added to (9) to cover the possible generation shortage of each zone. It is employed only when intra-zonal generation combined with the power exchanges via interconnections cannot cover the zonal load demands.…”

“…Reference [6] discusses how a FB domain is constructed considering the network model, input data, and parameter settings. The impacts of the internal (discretionary) parameters of the FB model on the FBMC outcomes are investigated in [7][8][9][10], which demonstrate how an optimal setting of such parameters can maximize the performance of the FBMC. In addition, a new method is proposed in [11] that aims to enhance the overall efficiency of the FBMC and its eventual remedial actions.…”

“…To integrate the cross-border exchange capacities defined by the FB domains into the probabilistic adequacy assessments, the main challenge consists in finding the FB domain that correctly represents the potentially binding grid constraints for the system conditions at hand. Indeed, the hourly FB domains depend on the network operating points as well as the internal (discretionary) parameters of the FB model (e.g., generation shift keys for nodal to zonal conversion [7], the minimum threshold for selection of the critical network elements [8], and the minimum branch capacities reserved for the power exchanges [9][10]). A direct model-based calculation of hourly FB domains first necessitates adopting assumptions on the abovementioned parameters that are partly known to a Transmission System Operator (TSO) as the FB calculation entails a sequential process that relies on the exchange of data among the involved TSOs.…”