District energy networks are undergoing a significant transformation. As cities across Europe and beyond push toward lower-carbon heat sources, large-scale heat pumps are moving from pilot projects into the operational core of district heating systems. Integrating a heat pump into an existing network is not simply a matter of connecting a new piece of equipment; it reshapes how pressure, temperature, and flow interact across the entire system. Heat pump integration modeling gives engineers the analytical foundation to understand these changes before they happen in the real network.

This article builds from the ground up: starting with what heat pump integration modeling actually is, moving through how large-scale heat pumps behave inside a district network, and progressing to the practical parameters, placement decisions, and control strategies that determine whether an integration succeeds. Whether you are evaluating a first heat pump installation or refining a model for a network that already includes one, each section builds on the last.

What is heat pump integration modeling?

Heat pump integration modeling is the process of representing a large-scale heat pump within a physics-based simulation of a district heating or district energy network so that engineers can predict how the heat pump will affect system-wide hydraulic and thermal behavior under a range of operating conditions.

The term covers more than just the heat pump itself. A complete heat pump integration model includes the heat pump unit, its connection points to the primary network, the source side (such as a river, seawater loop, or waste heat circuit), and the control logic that governs how the heat pump responds to network demand. All of these elements interact, which means a change in one part of the model produces measurable effects elsewhere in the network.

To understand why this matters, consider an analogy: adding a heat pump to a district heating network is similar to introducing a new pressure and temperature boundary condition into a hydraulic system. The network was originally balanced around a specific set of supply conditions. A heat pump operating at different temperature levels and with its own flow characteristics alters that balance. Without modeling the integration in advance, engineers have no reliable way to anticipate where pressures will shift, whether existing pumping capacity remains sufficient, or how the heat pump will perform across seasonal load variations.

Heat pump integration modeling addresses these questions systematically, using the same hydraulic simulation principles that govern the rest of the network model.

How large-scale heat pumps behave inside a district network

A large-scale heat pump in a district heating context operates as both a thermal source and a hydraulic element. On the thermal side, it extracts heat from a low-temperature source and delivers it to the district heating supply at a higher temperature. On the hydraulic side, it introduces flow, pressure, and temperature conditions that interact directly with the distribution network.

One characteristic that distinguishes large-scale heat pumps from conventional boilers or combined heat and power units is their sensitivity to supply and return temperatures. A heat pump’s coefficient of performance, the ratio of heat delivered to electrical energy consumed, decreases as the temperature lift increases. This means that a network operating with a high supply temperature and a warm return temperature will extract less efficiency from the same heat pump than a network with lower, tighter temperature levels. In modeling terms, this creates a feedback loop: the network’s thermal state influences the heat pump’s performance, and the heat pump’s output influences the network’s thermal state.

Large-scale heat pumps also introduce a distinct hydraulic signature. Unlike a boiler that can modulate output across a wide range with relatively minor hydraulic effects, a heat pump typically operates within a defined flow range on both its source and district heating sides. Operating outside that range, either too high or too low, affects both efficiency and equipment longevity. For a district heating network, this means the heat pump imposes constraints on how flow is distributed, particularly during partial load conditions when overall network demand is lower than design capacity.

For example, a heat pump connected to a seawater source in a coastal city will behave differently in winter, when source temperatures are lowest and network demand is highest, than in summer, when conditions reverse. Capturing this seasonal variation accurately is one of the core tasks of large-scale heat pump simulation.

Key parameters to define when modeling a heat pump

Accurate heat pump network modeling depends on defining the right input parameters. Omitting or approximating these parameters introduces errors that compound as the model is used for planning decisions.

The parameters that most directly shape model behavior fall into three groups:

• Thermal performance parameters: The heat pump’s rated heating capacity, coefficient of performance at defined operating conditions, and the performance curves that describe how output and efficiency change as supply temperature, return temperature, and source temperature vary. These curves are typically provided by the manufacturer and should be incorporated directly into the model rather than approximated with a fixed efficiency value.
• Hydraulic parameters: Flow rates on both the district heating side and the source side, pressure drops across the heat pump unit, and the operating range within which the heat pump can function. These values determine how the heat pump interacts with the network’s pressure profile and what pumping support it requires.
• Control parameters: The setpoints and logic that govern when the heat pump starts and stops, how it modulates output, and how it responds to signals from the network, such as changes in supply temperature demand or pressure at a reference point. Control parameters connect the physical model to the operational behavior of the system.

A common modeling error is treating the heat pump as a fixed heat source with a constant output. In reality, heat pump output varies continuously with operating conditions. A model that does not capture this variability will produce inaccurate pressure and temperature predictions, particularly during the seasonal transitions when conditions change most rapidly.

Why heat pump placement changes network behavior

Building on the hydraulic parameters described above, the physical location of a heat pump within a district heating network has consequences that extend well beyond the connection point itself. Placement determines how the heat pump’s output reaches consumers, what pressure conditions it operates under, and how it interacts with other heat sources in the system.

In a network supplied from a central production plant, adding a heat pump at a remote substation or at the edge of the distribution network creates a distributed generation scenario. Flow patterns that were previously unidirectional, from the central plant outward, may become bidirectional in sections of the network adjacent to the new heat pump. This changes the pressure difference profile across those pipe sections, which in turn affects how control valves and substations in that area function.

Pressure difference is the mechanical expression of balance between production, distribution, and demand in the network. When a heat pump is introduced at a new location, it alters this balance locally. Pressure anomalies that appear after integration, unexpected drops in differential pressure at consumer substations, or unusually flat pressure profiles in sections near the heat pump, are often symptoms of this rebalancing rather than faults in the heat pump itself. Identifying the source of these deviations requires comparing expected and measured pressure differences across the affected sections of the network.

Placement also affects the temperature distribution downstream of the heat pump. If the heat pump supplies at a lower temperature than the central plant, consumers served primarily by the heat pump may experience different temperature conditions than those served by the main supply. In a network where supply temperature is used as a control signal, this can create conflicts between the heat pump’s operating logic and the network’s overall control strategy.

For example, a heat pump placed at a mid-network injection point in a city with a star-topology distribution system will affect a defined subset of branches. The same heat pump placed at the central production site will influence the entire network simultaneously. These are fundamentally different integration scenarios, and they require separate modeling assessments.

Simulate heat pump control strategies and seasonal scenarios

The final dimension of heat pump integration modeling moves from static analysis to dynamic simulation: testing how the heat pump performs under different control strategies and across the full range of seasonal operating conditions the network will face.

Control strategy simulation addresses a practical question that static models cannot answer: how should the heat pump be operated to maximize its contribution to the network while maintaining hydraulic balance and supply quality? Common control strategies for district heating heat pumps include:

• Supply temperature control: The heat pump modulates output to maintain a target supply temperature at its connection point or at a reference location in the network.
• Base load operation: The heat pump runs at a defined constant output, with peaking sources covering the remainder of demand. This strategy maximizes heat pump operating hours and is common where electricity costs are predictable.
• Demand-responsive control: The heat pump responds to signals from the network, such as return temperature, pressure at a reference point, or a direct demand signal from a control system, and adjusts output accordingly.
• Source-temperature-optimized control: The heat pump’s setpoints are adjusted based on the temperature of the heat source, allowing it to operate at higher efficiency when source conditions are favorable.

Each of these strategies produces a different pattern of hydraulic and thermal behavior in the network. Simulating them in a district energy model allows engineers to compare outcomes before committing to a control configuration in the real system.

Seasonal scenario simulation adds the time dimension. A heat pump that performs well under peak winter demand may create hydraulic imbalances during low-demand summer periods, when network flow rates drop and the heat pump’s minimum flow requirement becomes a constraint. Simulating shoulder seasons, autumn and spring, is equally important, because these are the periods when multiple heat sources are likely to operate simultaneously and their interactions are most complex to manage.

Fluidit Heat is purpose-built for this type of district energy modeling, combining physics-based hydraulic simulation with the analytical tools needed to evaluate heat pump integration across multiple control strategies and seasonal scenarios. The platform’s architecture supports the full modeling workflow described in this article, from defining heat pump parameters to assessing the network-wide effects of different placement and control decisions.

If you are planning a heat pump integration project and want to assess how simulation can support your design and operational decisions, get in touch with our team to discuss your network and explore how Fluidit Heat can support your work.

Related Articles

• Heat network hydraulic modeling: how to cut project delivery time
• How to model temperature dynamics in a district heating network during load shifts
• Why is a digital twin important for district heating networks?
• How digital twins help energy and water utilities make smarter long-term investment decisions
• Hydraulic modelling and digital twins: real-time insights for district heating