National Energy System Model with Focus on Intersectoral System Development

»REMod«

 

Study | Fraunhofer ISE | November 2024

Paths to a Climate-Neutral Energy System

Federal States in the Transformation Process
(German Version)

Study | Fraunhofer ISE | February 2020

Paths to a Climate-Neutral Energy System

The German Energy Transition in its Social Context

Scheme of the energy system as presented in the simulation model of REMod
© Fraunhofer ISE
Scheme of the energy system as presented in the simulation model of REMod.

Analytical Focus

  • Cost-optimized structural transformation of (trans-)national energy systems towards greenhouse gas neutrality
  • Future year specific expansion paths of technologies as well as market share and technology trends
  • Localization of technologies in several regions (e.g. EU states or federal states) and expansion of the electricity and hydrogen grid
  • Technology-specific operational management strategies based on the interaction of sector-coupling technologies
  • Impact and system integration of sector-coupling technologies such as electric vehicles, electrolyzers, or electric heat pumps

Background of Model Application

To achieve the set climate protection goals, the European Commission and the German government have decided to fundamentally restructure the European and German energy system. The energy system model analyzes this transformation and its effects:

How can a cost-optimal transformation of a national or transnational energy system be achieved, considering all energy sources and consumer sectors, while meeting the declared climate targets and ensuring of a secure energy supply? The REMod model is ideally suited to answering specific questions on the transformation of (trans-)national energy systems due to its adaptable scenario analysis capabilities. Through repeated collaboration with federal and state ministries, international research institutions and especially through close exchange with industrial clients, the model has been continuously developed and has been proven to be an effective strategic consulting tool. 

Characteristics of the Model

The REMod model is based on a simulation-based cost optimization of (trans-)national energy supply systems whose CO2-emissions do not exceed a given budget and/or given annual reduction targets. The optimization objective is to scale all generators, storages, converters, and loads at minimum cost in such a way that the energy balance of the entire system is met at every hour. Each technology characteristic can be mapped in any level of detail. For example, different charging strategies for battery electric vehicles or the interaction of thermal storage with different heating systems can be realistically represented. In addition to ecological sustainability and economic efficiency, the model also considers security of supply through a high level of technical detail and temporal resolution. By incorporating real weather data, the influence of extreme weather years on the energy system is also taken into account. Similarly, a multi-node approach allows different regions (e.g., EU states or federal states) in the observation area to be mapped and their interactions to be studied, so that conclusions can be drawn about infrastructure measures. REMod offers the option of mapping individual countries or even entire continents. Currently, REMod is mainly used to map the German and European energy system.

The following Characteristics Describe the REMod Model

  • Technical focus: description of the interaction of the sectors energy, industry, buildings and transport sector on the transformation path until 2060
  • Objective: determination of the cost-optimal transformation of the (trans-)national energy system for a given reduction of greenhouse gas emissions
  • Type of energy system model: technical bottom-up energy system model with simulation-based optimization of expansion planning coupled with an hourly simulation of the operation
  • Geographical coverage: transnational (Europe), national (e.g., Germany) and regional (e.g., federal states states) possible
  • Temporal resolution: annual investments and hourly operation until 2060
  • All sectors: demand, generation, storage, energy conversion, demand-side management, and infrastructure
  • Framework: achieving a specified reduction in greenhouse gas emissions (budget and/or annual) and meeting energy demand on an hourly basis
  • Dealing with uncertainty: sensitivity and scenario analysis
  • Programming language: Julia / Python, Optimization algorithm: Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

Clients

  • Energy service providers
  • Industry
  • Political decision makers
  • Public institutions

References

Highlights