Solar-Institut Jülich
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This study focuses on thermoelectric elements (TEE) as an alternative for room temperature control. TEE are semi-conductor devices that can provide heating and cooling via a heat pump effect without direct noise emissions and no refrigerant use. An efficiency evaluation of the optimal operating mode is carried out for different numbers of TEE, ambient temperatures, and heating loads. The influence of an additional heat recovery unit on system efficiency and an unevenly distributed heating demand are examined. The results show that TEE can provide heat at a coefficient of performance (COP) greater than one especially for small heating demands and high ambient temperatures. The efficiency increases with the number of elements in the system and is subject to economies of scale. The best COP exceeds six at optimal operating conditions. An additional heat recovery unit proves beneficial for low ambient temperatures and systems with few TEE. It makes COPs above one possible at ambient temperatures below 0 ∘C. The effect increases efficiency by maximal 0.81 (from 1.90 to 2.71) at ambient temperature 5 K below room temperature and heating demand Q˙h=100W but is subject to diseconomies of scale. Thermoelectric technology is a valuable option for electricity-based heat supply and can provide cooling and ventilation functions. A careful system design as well as an additional heat recovery unit significantly benefits the performance. This makes TEE superior to direct current heating systems and competitive to heat pumps for small scale applications with focus on avoiding noise and harmful refrigerants.
Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.