Thermal Energy Storage

From system planning to investment decisions

District heating companies are under pressure to make long-term investment decisions in an uncertain energy landscape.

Electrification, fuel price volatility, and technologies such as heat pumps and thermal storage add complexity. Traditionally, investment studies rely on a single model type. System optimisation models identify the most cost-efficient production mix, while infrastructure models analyse hydraulic and thermal behaviour in the network and facilities.

Used separately, key interactions can be missed: a solution may look profitable but prove infeasible, or be feasible but uneconomic. Combining both perspectives provides a stronger basis for decision-making.

Combining two complementary models

The integrated approach combines two modelling tools. Ea Energy Analyses brings decades of experience with the energy system optimisation model Balmorel, which identifies least-cost operation and investment over time and calculates dispatch, fuel use, and costs with hourly resolution.

The second tool is the dynamic infrastructure simulation model, PFC Pro, in which NXITY is an expert. It represents consumers, the transmission and distribution network, and production facilities, simulating temperatures, flows, pressures, and transient events such as pump trips or rapid load changes.

Balmorel delivers optimal production and dispatch patterns, which are then validated in PFC Pro to ensure the infrastructure supports the strategy. Together, Ea Energy Analyses and NXITY link system economics with network behaviour.

Example: Long-term Development Analysis in the Greater Stockholm Region

This integrated modelling approach was used in a recent project in the Greater Stockholm Region to support long-term development of the district heating system in Täby. Production scenarios (technologies, fuels, capacities, and locations) and infrastructure development scenarios (pipe reinforcements and pump stations) were evaluated from both technical and financial perspectives, including thermal energy storage.

The energy system model assessed how storage can shift dispatch and reduce operating costs by avoiding more expensive production. The infrastructure model provided input on network limitations and validated the operational results, ensuring safe operation with respect to pump trips and valve closures under new conditions.

The modelling approach provides answers to suitable thermal storage type, temperature levels, size, dimensions, charge/discharge capacities, among others. The insights from the analysis provide a robust foundation for confident investment decisions.

Figure 1: Avoided annual operating costs due to thermal storage, expressed as a share compared to total operating costs without storage in the Täby district heating network.

Stronger decision support

Integrating system optimisation and infrastructure simulation offers several advantages:

• Improves the accuracy of investment analyses. Economic results are validated against system behaviour.
• Provides better operational insight. Operators can see how new assets affect dispatch, flow, and system flexibility.
• Strengthens risk assessment. Potential technical limitations can be identified early in the planning phase.
• Supports transparent decision-making. Results can be explained in both economic and technical terms.

The future of district heating planning

District heating systems are becoming increasingly complex. Electrification, sector integration, and climate targets require careful planning. Electricity is playing a growing role, as large heat pumps, electric boilers, and combined heat and power plants link heat production to the power market.

As a result, system economics are increasingly influenced by hourly electricity price fluctuations. Assessing electricity market dynamics together with district heating operation allows investments to be evaluated from a broader system perspective, including the interaction between heat production, storage, and electricity prices hour by hour.

Integrated modelling therefore provides stronger decision support, helping utilities test investments under different market conditions, including shifts in fuel or electricity prices. Such analyses can provide valuable insight when planning the next generation of district heating infrastructure.

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