Cost-optimal control of hybrid heating system in an energy community

Nowadays, several hybrid heating systems e.g. the combination of heat pumps and district heating are utilized in energy communities. The study analyzed the impact of three control strategies on energy usage and costs in a small energy community in a hybrid heating system. The results showed that the cost-effective control strategy based on hourly electricity prices and a price estimate of district heating brought up to 25% total costs reduction compared to the heat pump-prioritized control strategy.

Keywords: Hybrid heating, Heat pumps, district heating, Cost-optimalisation

Energy communities provide benefits ranging from increasing efficiency and the number of renewable energy sources to allowing energy end-consumers to participate actively in the energy market [1]. Integrating decentralized heat pumps and district heating (DH) into a hybrid heating system to cover heat demand of energy communities can help maximize the benefits of both heat sources [2]. Regarding DH, recovering waste heat from hydrogen production for DH production becomes promising considering the significantly expanded renewable hydrogen production in the future [3]. Control strategies are crucial for reducing energy costs in hybrid heating systems using multiple energy carriers.The study assesses the impact of cost-effective control strategies based on momentary electricity and district heat prices in the energy community with a hybrid heating system through dynamic simulation. In addition, it presents the effect of the price estimate of DH produced from hydrogen production waste heat on the cost-optimal control for an energy community.

Methodology

The small energy community on Aalto campus in Espoo, Finland (Figure 1) contains five educational buildings with many classrooms, office rooms, meeting rooms, and different laboratories. These buildings are served by a hybrid heating system, comprising a low-temperature heating (LTH) network and a DH network. The LTH network is powered by a dual source heat pump (DSHP) which utilizes both waste heat, condensing heat of cooling, from a laboratory and ambient air as heat sources.

Figure 1. Scheme of the analysed energy community.

The study utilized IDA ICE 5.0 to dynamically simulate individual buildings and the hybrid heating network. Each building was modelled separately using input data to generate hourly heat demand profiles for space, ventilation, and DHW heating. These profiles were then used as the inputs for the hybrid heating network model customized with integrated functional modules in IDA ICE. Simulations were conducted for the years 2022 and 2023. They were simulated with weather data Tapiola + Kumpula, 2022 and 2023.

In the analysis, energy costs for the small energy community were calculated based on the hourly electricity prices and monthly DH prices for 2022 and 2023, and a constant annual price-estimate of zero-emission DH (see Figure 2). The zero-emission DH was produced using recovered waste heat from an off-grid alkaline water electrolyzer (AWE) plant for hydrogen production in Finland [4]. In addition to energy costs, annual operating costs also took account DH power fee cost, which was calculated based on the price models from a Finnish energy company for 2022 and 2023 [5].

Figure 2. Energy prices in 2022 and 2023.

The analysed control strategies included an LTH-prioritized and two cost-effective control strategies based on different electricity and DH prices. For the LTH-prioritized strategy, the community utilizes LTH preferentially, while DH acts as the backup source if needed. Cost-effective control strategies mean utilizing either LTH or DH based on which possesses the lowest marginal cost for each hour as the prioritized heat source. According to these strategies, altogether six simulation cases were formulated shown in Table 1.

Table 1. Simulation cases based on different control strategies.

Results

Operation hours and power usage

Table 2 shows the operation hours and power usage of different heat sources in different simulation cases for 2022 and 2023. In the LTH-prioritized case, DH was prioritized only when heating demand came solely from DHW circulation loops. As shown in Figure 3, high electricity prices in the cost-effective case with commercial DH led to a significant reduction in LTH usage, making commercial DH more economical for much of the time. In the cost-effective case with zero-emission DH, its usage as the prioritized heat source increased further due to its lower price compared to commercial DH, especially for the heating season. Consequently, LTH was deprioritized even more. In 2023, the shift in operating hours from the LTH-prioritized to cost-effective cases was smaller than in 2022, mainly due to a narrower price gap between DH and electricity.

Table 2. Operation hours and power usage of different heat sources while using different control strategies in 2022 and 2023.

Figure 3. Duration curve of power usage in the LTH-prioritized case and the cost-effective case with commercial DH in 2022.

Regarding power usage, the maximum LTH power remained consistent across all cases and years, reflecting its production limit for the community. In cost-effective cases with commercial or zero-emission DH, DH was used more frequently to fully meet heating demand. It resulted in significantly higher peak DH power compared to the LTH-prioritized case. In 2023, peak DH power was lower than in 2022 across all cases due to higher minimum outdoor temperatures.

Energy consumption and operating cost

Energy consumption varied across simulation cases in both years (seeTable 3). In 2022, LTH and DH shared 52% and 48% of the heating demand, respectively, in the LTH-prioritized case. In cost-effective cases, the share of DH rose to 74% with commercial DH and 83% with zero-emission DH because DH was prioritized more frequently. Correspondingly, LTH covered less demand, and DSHP electricity consumption dropped significantly in both cases.

Table 3. Annual energy consumption and costs of different simulation cases in 2022 and 2023.

The total building heating demand was higher in 2023 due to colder weather conditions. The heating demand ratio covered by different heat sources changed less in cost-effective cases with commercial or zero-emission DH for 2023 compared to 2022. This was because the time shares that each heat source was prioritized remained more stable in 2023.

Table 3 also presents the annual energy and power fee costs in different simulation cases for 2022 and 2023. In 2022, more usage of DH led to higher energy cost in the cost-effective case with commercial DH. However, despite even greater DH use, the cost-effective case with zero-emission DH had lower DH energy costs due to its significantly cheaper price during the heating season.Higher peak DH power in the cost-effective cases also led to increased power fees. Overall, total energy and power costs declined progressively from the LTH-prioritized case to the cost-effective case with zero-emission DH, with reductions of 10% and 25% for the commercial and zero-emission DH cases, respectively.

In 2023, the trends in electricity and DH energy costs across cases were similar to those in 2022. Despite higher heating demand in 2023 compared to 2022, total energy costs did not increase due to significantly lower energy prices. However, DH power fee policies in 2023 led to much higher power fees across all cases even if peak DH power remained similar in both years. Thus, power fees accounted for a larger share of total costs. In the cost-effective case with commercial DH, the savings from price-based control could not fully offset the increased power fees, leading to a 5% higher total cost than the LTH-prioritized case. The cost-effective case with zero-emission DH still had the lowest total cost, saving 5% compared to the LTH-prioritized case.

Conclusion

According to simulation results, the cost-effective control strategy based on hourly electricity price and zero-emission district heat led to the lowest annual operating costs. It reduced the total costs by 25% in 2022 and 5% in 2023 compared with the low-temperature heating prioritized control strategy. The economic impact of cost-effective control strategies in different years is significantly affected by the momentary electricity and district heat prices and district heating power fee charging policies. Smaller price differences between electricity and DH reduce potential savings of energy costs. Changes in district heating power fee policies could significantly change the power fee cost, potentially weakening or even offsetting the cost-saving benefit acquired by cost-effective control strategies.

Acknowledgements

This study is part of the B2RECoM project (Grand number: 10784/31/2022). The B2RECoM project is mainly funded by Business Finland. In addition, companies and cities fund the project, including Aalto University Campus & Real Estate Ltd., ARCO Ltd., Elstor Ltd., Fortum Power and Heat Ltd., Gebwell Ltd., Granlund Ltd., Smart Heating Ltd., Sweco Finland Ltd., City of Helsinki, and City of Lappeenranta.

References

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