Purpose and Motivation

What is the purpose of the Mid-term Adequacy Forecast?

The Mid-term Adequacy Forecast (MAF) is a pan-European monitoring assessment of power system resource adequacy spanning a timeframe from one to ten years ahead. It is based upon a state-of-the-art probabilistic analysis, aiming to provide stakeholders with comprehensive support to take qualified decisions.

Resource adequacy assessments are increasingly prominent studies that use advanced methodologies to model and analyse rare events with potentially adverse consequences for the supply of electric power. They capture the continuous balance between net available generation, on the one hand, and net load levels, on the other, as shown in Figure 1.

Due to the increasing level of variable renewable energy sources in the European power system and the associated challenges for system development and operation, a pan-European analysis of resource adequacy has become ever more important. Cooperation across Europe is necessary to accelerate the development of common methodological standards, i.e., a common ‘language’ is needed for performing these studies.

Over the past decade, the European Network of Transmission System Operators for Electricity (ENTSO-E) has been improving its methodologies and forecasts continuously and will continue to ensure that further progress is made. MAF contributes to the harmonisation of resource adequacy methodologies across Europe by being a reference study for European Transmission System Operators (TSO) and a target approach for the Ten-Year Network Development Plan (TYNDP) and Seasonal Outlook studies. The MAF aims to provide stakeholders with the data necessary to make informed, quality decisions and promote the development of the European power system in a reliable, sustainable and connected way.

Figure 1

Figure 1: Resource adequacy: balance between net available generation and net load

Stakeholders should find the MAF and its extensive pan-European coverage particularly useful. In fact, MAF is the most comprehensive pan-European assessment of adequacy attempted to date, as it is based on a market-based probabilistic modelling approach undertaken in a collaborative effort with representatives from TSOs across the entire pan-European area. Five different modelling tools have been calibrated with the same input data and benchmarked against one another to increase consistency, robustness, and – fundamentally – confidence in the complex analytical results presented in the report.

It should be noted that the present pan-European assessment inevitably faces limitations. For instance, MAF does not consider all possible network constraints within a defined modelling zone. The higher granularity of national/regional adequacy assessments might be able to detect local resource or network constraints which cannot be identified by the present pan-European assessment, thus highlighting the complementary nature of regional/national and pan-European analyses. While such studies may rely on the same methododology and reference scenarios, they can assess additional sensitivities¹. National and regional studies can use tools and data granularity that are complementary to those used by ENTSO-E.

1 Regulation (EU) 2019/943 of the European Parliament and of the Council on the internal market for electricity, Chapter IV, Art. 20.1.

What are the main improvements compared to the MAF 2018?

Data granularity and quality have improved significantly in MAF 2019. For example, for the first time, unit-by-unit information concerning thermal generation was collected and implemented in the models. In addition, the climate database was extended to include 35 years of hydrological data, and a new, improved methodology was introduced to construct hourly demand time series.

Improvements in MAF 2019

Five modelling tools were used to performg the adequacy assessment in MAF 2019, and efforts to align models were intensified. Specific modelling improvements have also been implemented:

Data and Modelling Improvements

Thermal generation: One of the most prominent innovations of MAF 2019 is undoubtedly the increased granularity of the thermal generation data. A major milestone for ENTSO-E studies has been reached in the collection of thermal generation data with the highest granularity (unit-by-unit), including a wide range of information for each power plant property in the pan-European system. For MAF 2019, a hybrid implementation approach was followed: two models were built based on the new, more granular database, while the remaining three models were built on an aggregated version of the database using the same clustering used in MAF 2018, i.e., clustering by technology type.

Hydro modelling: Available hydropower generation is an important factor for assessing adequacy in power systems. It can have significant impact on results, which highlights the importance of choosing the appropriate level of detail, evaluating distinct hydrological conditions, applying harmonized assumptions and better reflecting the interdependence of hydro generation and climate conditions. Thus, a new hydro database was constructed for use in MAF 2019 which encompasses the full ENTSO-E perimeter with a complete set of distinct hydrological conditions for all climate years. The new hydro database guarantees homogeneous data, a common methodology for hydro modelling and a better reflection of the correlation of hydro output with other climate variables. This is a major improvement compared to MAF 2018, in which the weather’s impact on hydro generation was only considered through clustering climate years as wet, dry or normal.

Demand time series: An advanced demand modelling tool is introduced in MAF 2019, which builds on statistical modelling of historical data. The model of each zone is trained based on a sufficient number of historical demand time series in order to better capture the dependency of demand on parameters such as temperature and to construct the target time series for all available climate scenarios. The new methodology leads to more reliable projections of demand time series for the target years and better reflects the impact of climate variables and new technologies (e.g., electric vehicles) on demand.


Flow-Based Sensitivity: To improve the representation of the network in the simulations, a sensitivity analysis has been performed for target year 2021. This sensitivity analysis builds on the methodology introduced in PLEF² and presented in MAF 2018 but uses updated input data (Flow-Based domains). The corresponding simulation model was built to follow the rules of the Central Western Europe (CWE) Flow-Based Market Coupling and has been applied to the CWE perimeter. The results of this study are compared with the Base-Case scenario and provide additional insights into the impact of network considerations on market simulations. The results of this sensitivity analysis are presented in Appendix 1.

2 Pentalateral Energy Forum, Support Group 2, “Generation Adequacy Assessment”, January 2018,

Low-Carbon Sensitivity: The transition towards a European power system with high shares of renewable sources and decreased thermal capacities is driven by low-carbon emission policies (including carbon pricing). These policies have a significant impact on the availability and profitability of fossil-fuel thermal generation technologies in the power system. While this impact on installed capacity is already included in the Base-Case scenarios for MAF 2019, a sensitivity study was added that reflects a more ambitious trajectory for the phase-outs of fossil-fuel-generation capacities. The sensitivity study was based on the additional inputs of 11 countries. In line with the well-received Low-Carbon sensitivity study presented in MAF 2018, data were collected from TSOs for a scenario corresponding to a decrease in the installed capacity of coal and lignite for 2025. The input data and the results of this analysis are presented and compared to the Base-Case results in ­Appendix 1.

Monte Carlo: State-of-the-art technique to assess resource adequacy

The core idea of the Monte Carlo (MC) method is to use random samples of parameters or inputs to explore the behavior of a complex system or process. The high number of aleatory input variables which influence the outcomes of an adequacy assessment in power systems makes MC very suitable for the current study, where it is used to represent probabilistic variables such as unplanned outages in electricity market models. The process is illustrated below.

Monte Carlo Method

The “Clean Energy for all Europeans” package and evolution of MAF

Along with the coupling of European energy markets, integration of renewable energy sources and efforts to decarbonize energy systems, adequacy monitoring needs to be intensified. Within the current fast-paced landscape, the European resource adequacy assessment, i.e., the annual screening of adequacy in Europe for the upcoming decade, must provide input for strategic decisions regarding, for instance, the introduction of capacity mechanisms.

Therefore, the methodology for assessing adequacy in Europe will need to undergo significant changes in order to address the challenges the energy sector faces.

What are the upcoming challenges and future steps for resource adequacy?

Economic viability, capacity mechanisms, increased temporal granularity, more sensitivities: undoubtedly, the “Clean Energy Package” aims high, introducing significant challenges and improvements for future pan-European and regional adequacy assessments.

The recent legislative package on Clean Energy for all Europeans, specifically Regulation 2019/943 of 5 June 2019 on the internal market for electricity, has placed resource adequacy in a central position in European energy policy. Under this regulation (Article 23), European resource adequacy assessments are required to consider, amongst others, the following aspects:

__ Time horizons of 10 years with annual resolution;

__ Flow-Based modelling of the power network (when applicable);

__ An economic viability assessment of generation assets;

__ Analysis of additional scenarios, including the presence or absence of capacity mechanisms;

__ Consideration of energy sectoral integration.

In addition, and to complement the European resource adequacy assessment, ENTSO-E shall develop a methodology for the definition of the value of lost load, the cost of a new entry in generation and/or demand response and a methodology used to establish the reliability standard.


MAF 2019

Mid-term Adequacy Forecast 2019

Executive Summary

Presents the motivation of the MAF 2019, followed by the main adequacy results for the Base-Case scenarios for the target years 2021 and 2025.

Download Executive Summary (PDF, 1.9 MB)
Appendix 1 – Detailed results, sensitivities and input data

Presents a closer look at the input data and the results of MAF 2019.

Appendix 1
Download Appendix 1 (PDF)
Appendix 2 – Methodology

Presents the main methodology followed in MAF 2019.

Appendix 2
Download Appendix 2 (PDF)
Appendix 3 – Country views on MAF 2019

Contains country-specific comments and relevant references to national and regional studies provided directly by the TSOs.

Appendix 3
Download Appendix 3 (PDF)
Summary slides of MAF 2019

Discover the key takeaways of MAF 2019.

Summary Slides
Download Summary Slides (PDF)

This will lead you to the consultation page and later on to its results.

Teaser Consultation
Visit Website
Resource adequacy target methodologies
Teaser Methodology
Visit Website
Input Data

The MAF study is built on several datasets, whereas a part of these is made available for the public. These datasets and detailed descriptions of preprocessed inputs can be accessed below.


ENTSO-E and the participating TSOs have followed ­accepted industry practice in the collection and analysis of available data. While all reasonable care has been taken in the preparation of this data, ENTSO-E and the TSOs are not responsible for any loss that may be attributed to the use of this information. Prior to taking business decisions, interested parties are advised to seek separate and independent opinions with respect to topics covered by this report and should not rely solely upon data and information contained herein. Information in this document does not amount to a recommendation with respect to any possible investment. This document is not intended to contain all the information that a prospective investor or market participant may need.

ENTSO-E emphasises that ENTSO-E and the TSOs involved in this study are not responsible in the event that the hypotheses presented in this report or the estimations based upon these hypotheses are not realised in the future.


ENTSO-E, the European Network of Transmission System Operators for Electricity, represents 43 electricity transmission system operators (TSOs) from 36 countries across Europe.

ENTSO-E was established in 2009 and was given legal mandates by the EU’s Third Legislative Package for the Internal Energy Market, which aims to further liberalise the gas and electricity markets in the EU.

Any question? Contact us:

+32 2 741 09 50