Hybrid energy system cost-optimal dimensioning for a Finnish kindergarten

Keywords: Heat Pump, Cost Optimization, Waste Heat from Hydrogen Production, District Heating

This study investigates cost-optimal dimensioning of a kindergarten hybrid energy system over a 25-year life cycle. Findings show high peak power demands make power fees dominate the total district heating costs. Consequently, varying district heat tariffs—considering waste heat integration—minimally impact the dimensioning, potentially undermining the waste heat utilization in district heating.

Due to the advantages of renewable sources for achieving carbon neutrality, their proportion in energy generation is rising. By 2030, the EU aims to create 20 million tons of hydrogen (European Commission, 2024). There is considerable opportunity of excess heat utilization. This study examines the cost-optimal dimensioning of a hybrid building energy system utilizing waste heat from hydrogen production, based on a 25-year life cycle cost (LCC) analysis. The hybrid energy system comprises a primary heating unit, a ground source heat pump (GSHP), and a district heating (DH) backup for a kindergarten. Two district heat tariffs: a commercial district heat pricing from a district heating company and a zero-emission district heat price derived from waste heat for hydrogen production were selected for comparative purposes.

Building parameters

The examined kindergarten locates in Lappeenranta, Finland. Table 1 collects the building main parameters. This study assumed the connection of an existing district heating network with supply temperatures of 115/70°C.

Intermittent mechanical balanced ventilation with heat recovery is in operation. It operates for two hours at maximum capacity by a constant air volume system to remove pollutants, after that transitioning to utilizing CO₂-based variable air volume control throughout operational hours. Consequently, there are significant heating power peaks in the early morning.

Simulation tool and weather conditions

Software IDA Indoor Climate and Energy (IDA ICE) version 5.0 with embedded optimization tool AutoMOO were used for the building simulation and LCC minimization (Ju et al., 2025). This study used hourly weather data from the Finnish Meteorological Institute (2023) station of Lappeenranta. The minimum temperature was −19.8°C as shown in Figure 1 with the annual average of 5.3°C.

Table 1. Properties of the simulated building.

Figure 1. Lappeenranta outdoor temperatures for the year 2023.

Decision variables and objective function

Table 2 collects the continuous decision variables and constraints of the hybrid system for dimensioning. The aim of LCC optimization is to search the cost-optimal solution under different variables related cost data.

Table 2. Units and constraints of the system for LCC optimization.

The objective function for the optimization is the find the minimum net present value (NPV) of 25-year LCC which includes the investment, maintenance, and renewal cost data (Ju et al., 2025).  The maximum constraint for GSHP and DH was defined based on the dimensioning heating demand (Table 1). Their design capacity relationship also follows the rule as shown in Eq. (1) during the simulation.

Where

Pdim. is the dimensioning heating power, kW

PHP is the heat pump design capacity for heating, kW

PDH is the district heating design capacity, kW.

Price data of electricity and district heating

The optimization utilized Nord Pool (2024) day-ahead hourly electricity prices. The electricity generated by the photovoltaic panels was initially delivered to the building, with any surplus sold to the market.

Figure 2 presents the monthly commercial district heat prices (Fortum, 2024) and the zero-emission district heat prices (considered as CO₂ emission free) (Meriläinen et al., 2024). The zero-emission price is much lower than the commercial price in winter.

The total DH costs consist of the district heat energy cost (calculated by the commercial or zero-emission district heat price) and power fee defined in Eq.(2):

Where

FA is the annual power fee (24%VAT), €;

FM is the monthly power fee, 6.2 €/kW (24%VAT)

PDH max is the maximum district heating supply power for the year to the kindergarten, kW

Figure 2. DH prices with 24% VAT.

Results

Figure 3 shows the cost-optimal solutions of LCC varying with heat pump design capacity for the kindergarten. It highlights that changing to the zero-emission district heat price reduces the heat pump design capacity and minimum LCC.

Figure 3. Cost-optimal solutions, net present value (NPV) of life cycle cost (LCC) varying with heat pump design capacity.

Table 3 list the results of minimum LCC and cost-optimal dimensioning capacities for GSHP DH, and PV. The utilization of the zero-emission district heat tariff decreases the 25- year LCC about 7% for the reference cases. In the cost-optimal cases for the kindergarten, the GSHP design capacity just slightly reduces after changing to the zero-emission district heat price. The percentage values in the brackets are the ratio of GSHP or DH design capacity among the total dimensioning heating power.

There are the DH costs when the LCC is minimum as shown in Figure 4. The total district heating cost consists of the energy costs calculated for the two district heat tariffs and the power fee by Equation (2). The power fee primarily varies according to the highest DH supply power. In the reference cases, the power fee allocation for the kindergarten exceeds 60%. It is prevalent in the district heat cost analysis. The high peak heating demand arises from the early morning purging ventilation. The dominant effect of the power fee leads to a little decrease in GSHP design capacity when a lower district heat price is applied. It signifies a challenge in recovering waste heat from hydrogen production for use in DH.

Table 3. Minimum life cycle cost and design capacity.

Figure 4. DH costs when the LCC is minimum.

Conclusion

The study examined the hybrid energy system (GSHP+DH) cost-optimal dimensioning for a kindergarten in Finland. The analysis contains a comparison of two district heat tariffs: the commercial district heat price and the zero-emission district heat price derived from the district heating production cost coupling with the waste heat generated during hydrogen production. The cost-optimal dimensioning of ground source heat pump accounts for approximately 20% of the overall dimensioning power. The alteration of district heat tariffs has minimal impact on sizing, as the power fee accounts for over 90% of the total energy costs for the hybrid system. This highlights that the high peak heating power demand can undermine the waste heat utilization in district heating.

Acknowledgement

Authors would like to thank for the financial contribution from the B2RECoM project (Grant No. 10784/31/2022) mainly funded by Business Finland. The authors would like to gratefully acknowledge the partners of the B2RECoM and BLOCKCC projects.

References

European Commission. Hydrogen. (2024).  Available at: https://energy.ec.europa.eu/topics/energy-systems-integration/hydrogen_en  (accessed on 06.08.2024).

Finnish Meteorological Institute. Weather and Sea—Download Observations. (2023). Available online: https://en.ilmatieteenlaitos.fi/download-observations (accessed on 30.08 2024).

Fortum. Kaukolämmön hinnat taloyhtiöille ja yrityksille (District heating prices for building societies and companies). (2024). Available at:  https://www.fortum.fi/yrityksille-ja-yhteisoille/lammitys-ja-jaahdytys/kaukolampo/kaukolammon-hinnat  (in Finnish, accessed on 25.07.2024).

Ju, Y., Hu, X., Jokisalo, J., Kosonen, R., Xue, T., Meriläinen, A., & Kosonen, A. (2025). Cost-optimal dimensioning of hybrid heat pump systems utilizing waste heat from hydrogen production for a kindergarten in cold climate. Energy and Buildings, 332, 115430.

Meriläinen, A., Kosonen, A., Jokisalo, J., Kosonen, R., Kauranen, P., & Ahola, J. (2024). Tech-no-economic evaluation of waste heat recovery from an off-grid alkaline water electrolyzer plant and its application in a district heating network in Finland. Energy, 306, 132181.

Nord pool. Day-ahead prices. (2024). Available at:  https://data.nordpoolgroup.com/auction/day-ahead/prices?deliveryDate=2024-02-16&currency=EUR&aggregation=Hourly&deliveryAreas=AT,FI  (accessed on 25.07.2024).