How many zones should an electricity market have? A cross-country perspective on bidding zone design

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

The Core Flow-Based Market Coupling Project (Core FB MC) focuses on the development and implementation of the day-ahead market activities in the Core Capacity Calculation Region (Core CCR) within the Single Day-Ahead Coupling (SDAC). The Transmission System Operators (TSO) and Nominated Electricity Market Operators (NEMO) of the Core CCR aim to introduce FB MC at the regional level and ultimately, to facilitate the merger of regional markets and the creation of a fully integrated European Internal Energy Market.
 Flow-Based Market is currently deployed within the Single Day-Ahead Coupling across Central Western Europe (CWE) covering Austria, Belgium, France, Germany, Luxemburg and the Netherlands. Within the Core Flow-Based Market Coupling project, all borders of the Core CCR will be coupled based on the Flow-Based capacity calculation methodology developed in the framework of the Capacity Allocation and Congestion Management (CACM) Regulation. The Core CCR consists of the bidding zone borders between the following EU Member States` bidding zones: Austria, Belgium, Croatia, Czech Republic, France, Germany, Hungary, Luxembourg, the Netherlands, Poland, Romania, Slovakia and Slovenia.
 The Flow-Based Methodology is a methodology in which physical network constraints are obtained based on the available constraints on the critical elements of the network (branch) and the power transmission allocation factors that are defined for each critical branch and each zone (Bidding Zone) within Core CCR. These factors describe how the position of each bidding zone changes when the energy flow in the critical branch changes. The computer algorithm then seeks an optimal exchange of energy between the bidding zones.
 Compared to the existing Net Transmission Capacity (NTC) methodology, the FB methodology takes into account multiple parameters and optimization conditions and therefore better reflects the actual circumstances in the network. The methodology for calculating the flows of power derives from the Commission Regulation (EU) 2015/1222 on the Establishment of CACM regulation (Article 20) and represents an important part of the European Target Model. The methodology contributes to reducing the price differences between national electricity markets, which leads to more stable prices, and increases the social benefits of the involved countries.
 This paper will provide historical overview of the Core FB MC project, which is based on the decision of the Agency for the Cooperation of Energy Regulators (ACER) on November 17th, 2016. It will further on provide a presentation of all the working groups and their tasks within the FB MC project, high-level architecture and information flows, a detailed description of test processes divided into separate test
 Given that the project is still in the implementation phase, and that Croatia is part of it since 2016, article will look at the local perspective and expectation from the moment of realization of the project, but will as well give an overall progress picture of the energy market and energy trading for the entire Core region.

The negative impact of increasing market shares of wind and solar power on their market values in electricity spot markets, i.e., the cannibalization effect, has been described and quantified extensively using both historical market data and energy market models. Yet, most previous work is limited to estimations on an aggregated level and does not account for heterogeneity of solar and wind power plants within one bidding zone. Applying panel data methods to a unique dataset with unit-wise revenues of variable renewable power plants in the Iberian electricity spot market in 2020 and 2021 that also includes unit characteristics, namely location, installed capacity and partially technology, I study the impact of heterogeneity within renewable power plants on their market values. As a result, this paper provides evidence that the effect of location or technology on renewables’ market values is almost as high as or even higher than the effect of the contemporary market share of renewable production. In terms of energy policy, this result underlines that integrating renewables in wholesale markets can incentivize choosing renewable projects’ technologies and locations according to price signals. For electricity market models, it provides insights into the relevance of heterogeneous production profiles when studying variable renewables’ market performances.

Strategic Bidding Zone Configuration for Enhanced Grid Efficiency: A Case Study of Germany's Electricity Market

In Europe, the ongoing renewable expansion and delays in the planned grid extension have intensified the discussion about an adequate electricity market design. Against this background, we jointly apply an agent-based electricity market model and an optimal power flow model to investigate the long-term impacts of splitting the German market area into two price zones. Our approach allows capturing long-term investment and short-term market behavior under imperfect information. We find strong impacts of a German market splitting on electricity prices, expansion planning of generators and required congestion management. While the congestion volumes decrease significantly under a market split in the short term, the optimal zonal configuration for 2020 becomes outdated over time due to dynamic effects like grid extension, renewable expansion and new power plant investments. Policymakers and regulators should therefore regularly re-assess bidding zone configurations. Yet, this stands in contrast to the major objective of price zones to create stable locational investment incentives.

Impact assessment of a minimum threshold on cross-zonal capacity in a flow-based market

In this paper an optimization problem designed to calculate electric grid specific indicators to be used within model-based methodologies for the definition of alternative electricity market bidding zone configurations is designed. The approach integrates within the framework of a bidding zone review process aligned to the specifications of the Commission Regulation (EU) 2015/1222 (CACM) and Regulation (EU) 2019/943 of the European Parliament and of the Council (CEP). The calculated solution of the optimization provides locational marginal prices and allows to determine, outside the optimization problem, the power transfer distribution factors for critical elements. Both indicators can be used as inputs by specially designed clustering algorithms to identify model-based electricity market bidding zone configurations, as alternative to the current experience-based configurations. The novelty of the optimization problem studied in this paper consists in integrating the N-1 security criteria for transmission network operation in an explicit manner, rather than in a simplified and inaccurate manner, as encountered in the literature. The optimization problem is evaluated on a set of historical and significant operating scenarios of the Italian transmission network, carefully selected by the Italian transmission system operator. The results show the optimization problem capability to produce insightful results for supporting a bidding zone review process and its advantages with respect to simplified methodologies encountered in the literature.

Improper configuration of bidding zones can lead to market efficiency losses, hinder the integration of renewable energy sources (RESs), and reduce grid security. To evaluate the impact of different bidding zone configurations on market performance, we developed a multi-dimensional evaluation framework containing a series of indicators covering aspects of market efficiency, grid security, and sustainability. These indicators facilitate the comparisons among different market dispatch mechanisms. To validate the proposed framework, the reconfiguration of the Italian bidding zones has been applied to a simplified Italian grid model to compare the market performance under different bidding zone configurations. The simulation results indicate that the implemented reconfiguration has led to enhanced market efficiency and security in the Italian power system. However, the reconfiguration shows a comparatively lower reduction in greenhouse gas (GHG) emissions, suggesting a weaker sustainable performance.

This paper focuses on model-based approaches that could be adopted for identifying alternative configurations to be considered in a bidding zone review process. Considering the complexity of this task, automated procedures can significantly help transmission system operators, allowing them to assess a large amount of possible system conditions and future scenarios. These methodologies are based on a 2-step approach: in the first step relevant nodal quantities are computed; then, in the second step, nodes are aggregated into zones using proper clustering algorithms. This paper starts with a critical review of the existing proposals, highlighting advantages and disadvantages of each of them, focusing on their practical implementation on a wide-area power system and in the context of the current electricity market framework. Some promising options are then identified for the Italian Power System case. In particular, an improved security constrained optimal power flow algorithm for computing Locational Marginal Prices (LMPs) and for identifying relevant critical branches (to be considered in the Power Transfer Distribution Factors computation) has been developed. Then, a selected set of clustering algorithms has been implemented and tested to check their effectiveness in forming LMP-based bidding zones.

This paper studies the inclusion of grid constraints into the internal European market clearing algorithms, using an optimization-based approach to provide a comprehensive and comparative analysis of different approaches, namely, Available Transfer Capacity (ATC), Flow-Based (FB), and hybrid ATC+FB. Specific EUPHEMIA-like algorithms are developed and the analysis considers both the social welfare maximization and power exchanges feasibility, the latter validated with an AC power flow. The case study includes real network data from Portugal, Spain, France, Belgium and Germany-Luxemburg bidding zones. Market data made available by MIBEL was used to model two compound Poisson processes representing electricity spot market bidding process and to generate five bidding scenarios, with the number of steps adjusted proportionally to the demand and generation in each bidding zone. The network model was built from the data available in the “Input grid datasets for the preparation of the Ten-Year-Network-Development-Plan (TYNDP) 2018” made available by ENTSO-E. The results show that the FB approach presents a higher social welfare value and feasibility when compared to the ATC approach. When considering the ACT values provided by ENTSO-E transparency platform for Iberian Peninsula, as it is done in the Single DayAhead Coupling (SDAC), the hybrid ATC+FB approach compares well with the FB approach in terms of both social welfare and AC power flow feasibility.

Due to the changes in the energy system the bidding zone configuration of the European electricity market must be evaluated periodically. According to the definition, bidding zones must not include structural congestions. In literature, one common approach to determine bidding zone configuration is by clustering nodal prices. Respecting different market setups around the world, it comes to mind that the components respected in nodal prices are not universal. Therefore, this paper investigates the effect selected grid security components and considering losses have on the nodal prices and bidding zone configuration. The investitigations are carried out on different test grids with varring number of meshes in the grid. It can be seen that the impact of losses can be neglected. In regards to the security components, we show that the influence decreases significantly while increasing the number of meshes. This is independent of the resulting powerflow in the grid model.

The European energy market is based on the zonal system. This implies that only exchanges between the bidding zones are subject to an allocation mechanism. The key question in bidding zone delimitation is which exchanges need to be subject to an allocation mechanism, and which exchanges can be left outside the allocation mechanism. In the recent years, tremendous progress has been made by CWE and CEE TSOs to develop the flow-based (FB) capacity calculation methodology. The resulting FB capacity constraints represent the TSO's constraints in the allocation mechanism. With a flow-based approach, a true competition between all relevant exchanges for the scarce capacity can be established. As such, bidding zone delimitation and FB capacity calculation and allocation are two key ingredients that are at hand to establish an efficient allocation mechanism for the European integrated electricity market.

Large-scale energy system modelling plays a crucial role in the debate on energy system decarbonisation. It is common to use a bidding zone representation to model the European energy system due to the structure of the electricity market. However, this may underestimate infrastructure constraints at higher spatial resolutions. This question has been investigated in the literature, while methods for aggregating highly resolved data remain a research gap. In this study we explore various spatial resolutions using the sector-coupled energy system model Balmorel with a focus on the Danish energy system. The modelling framework will encompass detailed geospatial data of existing Danish power plants in combination with the atlite module for generating variable renewable energy (VRE) production profiles at different geographical locations. Utilising the further developed modelling framework in Balmorel, the impact of applying various spatial resolutions is thus investigated from a bidding zone, NUTS2, NUTS3, to municipal spatial resolution. Preliminary results indicate that transmission costs are underestimated at low spatial resolution. However, they remain a small part of total system costs at very high spatial resolution. Large operational differences are observed, which will be investigated further. These results will be discussed considering spatial aggregation methods and used to inform further research on a similar investigation at the European scale to advance the modelling of sector-coupled energy system models with high penetrations of VRE.  

Decarbonisation policies aim at reducing fossil fuel based generation in favour of cleaner renewable energy sources. Changes in the generation mix to supply future electricity demand will require tools capable to emulate the bidding behaviour of new generation plants. Price forecasting tools lacking this feature and only based on historical data time series might soon become not satisfactory for this scope. This paper presents a methodology that, by considering hourly electricity generation offers (price, volumes) datasets, allows simulating future electricity wholesale's prices. This is done by taking into account new generation units and the dismissing of old (coal-based) units according to the demand and generation forecasts in the European Ten Year Network Development Plan (TYNDP) 2030 scenarios. Machine learning, clustering and distribution sampling techniques are used in this work to finally estimate prices distribution in 2030 in the biggest bidding zone of the Italian market. The results suggest that the prices obtained in the different scenarios do converge to those estimated by the TYNDP. The approach used bypasses the need to have access to all the transactions of a given market. Probability distributions are in fact enough in the proposed methodology to achieve similar results to those based on full knowledge of transaction datasets.

Time-evolution and forecasting of environmental and energy performance of electricity production system at national and at bidding zone level