BalticREStore

The Baltic energy system is small but diverse, with widespread district heating and continued reliance on external energy sources. In 2025, the region disconnected from Russia’s power system, removing a major source of inertia and creating new operational challenges. This case study examines integrating large-scale wind power under military-related development constraints. High spatial and temporal resolution data allows capturing detailed wind behavior and short-term intermittency.

The study evaluates how energy storage technologies can smooth variability and support grid operation. To demonstrate Mopo tool capabilities, an existing Backbone-based Baltic model is being translated into the INES format, updated with additional data and converted to SpineOpt. Scenario simulations, including the high-resolution regional case, illustrate the modeling workflow, interoperability features, and enhanced analytical capabilities of the Mopo tools.

• Geographical resolution: Baltic countries (Estonia, Latvia, Lithuania) and their interconnections with neighbouring systems, using cross-border exchange data from a Pan-European model. The electrical system is captured at the country level, while the heating system is represented with more spatial granularity, distinguishing capital-city areas and additional regional zones to account for differences in heat demand and supply structures.
• Sector resolution:
  • multiple energy sectors and conversion pathways: power, heating, gas, and Power-to-X (P2X) systems, buildings (space heating, hot water, efficiency measures) and transport
  • comprehensive set of commodities: natural gas, biogas, hydrogen, biomass, waste, coal, oil shale/retort gas, oil products (gasoline, diesel, ethanol, biodiesel)
  • carbon dioxide emissions from all relevant processes are explicitly represented.
• Temporal resolution: 2050 for the core modelling horizon. 15-minute intervals for the region-specific scenario.

The scenario explores the transition of the Baltic energy system toward high renewable penetration, with a particular focus on integrating a large-scale wind power plant located in a specific NUTS 3* region subject to military-related development constraints.

The electrical system is captured at the country level, while the heating system is represented with more spatial granularity, distinguishing capital-city areas and additional regional zones to account for differences in heat demand and supply structures. To analyse region-specific impacts in detail, Latvia’s capital heating network is divided into two subsystems. A specific NUTS 3 region is explicitly separated to reflect local constraints and development potential under local military-related restrictions.

These constraints restrict turbine height, placement, radar visibility, and supporting grid infrastructure (including off/on-shore transmission limitations), resulting in a narrower feasible siting area and potentially higher curtailment risks. The scenario evaluates how these limitations shape system flexibility needs, network planning, and investment decisions. The core modelling horizon is 2025 with simulations primarily conducted at hourly resolution to capture renewable variability and cross-sector interactions. For the region-specific scenario, time resolution is refined to 15-minute intervals, allowing more precise analysis of wind intermittency, system balancing needs, and short-term flexibility requirements. Wind and solar generation are modelled using detailed geospatial resource layers, ensuring a realistic representation of local intermittency patterns.

The scenario further examines how different storage technologies (batteries, P2X-based hydrogen storage, hydro/pumped storage) interact with intermittent supply under constrained grid conditions.

From a system perspective, the scenario extends beyond electricity: it incorporates sector coupling with the heating, gas, and transport sectors, reflecting long-term decarbonization pathways toward 2050. The regional heating system (especially the capital area) is split into subregions to better represent local thermal demand, district heating networks, and their interaction with CHP and heat pump resources.

*NUTS 3 is the third level of the EU Nomenclature of Territorial Units for Statistics, representing sub-regional territorial units.