Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

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This work examines a high temperature latent heat storage system, which could find use in future concentrated solar power and other combined heat and power plants. In contrast to lab-based fully charged or totally discharged states, partial load states will be the principal operation states in real-world applications. Hence, a closer look on the partial load states and the effective power rates are worthwhile for a successful implementation of this storage type. A vertical finned shell and tube heat exchanger pipe with a combination of transversal and longitudinal fins is applied. Sodium nitrate with a melting temperature of 306 is used as phase change material and thermal oil serves as heat transfer fluid. Temperatures in the storage and the heat transfer fluid as well as the mass flow are measured for data analysis. The state of charge formulation is based on an enthalpy distribution function, where the latent heat of fusion is spread over a specific temperature range. The data show consistently high power rates for all partial load cycles at any state of charge. The mean power rate for charging is 6.78 kW with an 95.45 % confidence interval of 1.14 kW for all cycles. The discharging power rate is −5.72 kW with a 95.45 % confidence interval of 1.36 kW for all cycles. The lowest power rate is measured for the full cycle at the end of charging/discharging. It is caused by a narrow volume, which is not penetrated by fins, near the perimeter of the cylindrical heat exchanger. The state of charge formulation correlates with the storage capacity and enables state of charge based cycling. With the energy balance of the storage, the data validity is proven and further storage parameters are determined. The energy density is as high as 110 kW h m −3 and a power rate of 2.28 kW m −1 for the finned tube is confirmed. These values are highly promising for further development and application of latent heat storage systems.

The sandTES technology utilizes a fluidized bed counter current heat exchanger for thermal energy storage applications. Its main feature is an imposed horizontal flow of sand (SiO2) particles fluidized by a vertical air flow across a heat exchanger consisting of several horizontal rows of tubes. Past international research on heat transfer in dense fluidized beds has focused on stationary (stirred tank) systems, and there is little to no information available on the impact of longitudinal or helical fins. Previous pilot plant scale experiments at TU Wien led to the conclusion that the currently available correlations for predicting the heat transfer coefficient between the tube surface and the surrounding fluidized bed are insufficient for the horizontal sand flow imposed by the sandTES technology. Therefore, several smaller test rigs were designed in this study to investigate the influence of different tube arrangements and flow conditions on the external convective heat transfer coefficient and possible improvements by using finned tubes. It could be shown that helically finned tubes in a transversal arrangement, where the horizontal sand flow is perpendicular to the tube axes, allows an increase in the heat transfer coefficient per tube length (i.e., the virtual heat transfer coefficient) by a factor of 3.5 to about 1250 W/m2K at ambient temperature. Based on the literature, this heat transfer coefficient is expected to increase at higher temperatures. The new design criteria allow the design of compact, low-cost heat exchangers for thermal energy storage applications, in particular electro-thermal energy storage.

For operation planning in industrial energy systems mixed integer linear programming (MILP) is the go-to method because of its reliability and the huge advances in MILP algorithms in recent years. MILP is especially well suited for planning the use of storage units, even if including the non-linear operating behavior of thermal storages is still a big challenge -especially if partial load cycles are considered. To model the storage behavior, a multi-variate non-linear function has to be linearized and incorporated into the MILP model. The key for good performance in MILP is using as few linear pieces as possible to achieve the required accuracy. We consider two types of piecewise-linear models: triangulation on a grid and general triangulation. In this paper, we present different heuristics for computing efficient piecewise-linear approximations of nonlinear functions. As a use case we consider the behavior of a thermal storage unit. We apply the heuristics to compute piecewise-linear approximation of the non-linear operating behavior and discuss the results. We then compare the performance of the models in a MILP model for the operation planning of an energy system. For translating the piecewise-linear function to MILP we consider state-of-the-art approaches with a logarithmic number of binary variables. Our results show that gridded triangulation models in combination with logarithmic MILP formulations can be used for data-driven modeling of non-linear operating behavior of devices. We highlight the potential of this approach for realizing adaptable operation optimization of energy systems.