• Energy Research

Journal of cleaner production 306, 127191 (2021). doi:10.1016/j.jclepro.2021.127191 Published by Elsevier Science, Amsterdam [u.a.]

Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom for technical system design and operation. We present COMANDO, an open-source Python package for component-oriented modeling and optimization for nonlinear design and operation of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks as surrogate models in a reduced-space formulation for deterministic global optimization. 24 pages, 1 graphical abstract, 13 figures, 4 tables

The metal–organic frameworks (MOFs) MIL‐101(Cr) and NH2‐MIL‐125 offer high adsorption capacities and have therefore been suggested for sustainable energy conversion in adsorption chillers. Herein, these MOFs are benchmarked to commercial Siogel. The evaluation method combines small‐scale experiments with dynamic modeling of full‐scale adsorption chillers. For the common temperature set 10/30/80 °C, it is found that MIL‐101(Cr) has the highest adsorption capacity, but considerably lower efficiency (−19%) and power density (−66%) than Siogel. NH2‐MIL‐125 increases efficiency by 18% compared with Siogel, but reduces the practically important power density by 28%. From the results, guidelines for MOF development are derived: High efficiencies are achieved by matching the shape of the isotherms to the specific operating temperatures. By only adapting shape, efficiencies are 1.5 times higher. Also, higher power density requires matching the shape of the isotherms to create high driving forces for heat and mass transfer. Second, if MOFs’ heat and mass transfer coefficients could reach the level of Siogel, their maximum power density would double. Thus, development of MOFs should go beyond adsorption capacity, and tune the structure to the application requirements. As a result, MOFs could to serve as optimal adsorbents for sustainable energy conversion.

Organic Rankine Cycles (ORC) use low-temperature heat to generate electrical power. To use the full potential of a heat source, the ORC has to be tailored to the specific application. Tailoring a cycle means an integrated design of both process and working fluid. This integrated design leads to complex mixed-integer nonlinear program (MINLP) optimization problems. To avoid this complexity, working fluid candidates are commonly preselected using heuristic guidelines; subsequently, the process is optimized for the set of preselected working fluids. However, the preselection can fail, leading to suboptimal solutions. An approach for integrated design of ORC process and working fluid is the Continuous-Molecular Targeting–Computer-aided Molecular Design (CoMT-CAMD) approach. CoMT-CAMD employs the physically-based Perturbed-chain Statistical Associating Fluid Theory (PC-SAFT) equation of state as thermodynamic model of the working fluid. In PC-SAFT, each working fluid is described by a set of pure component parameters. In a first step, the so-called CoMT step, the discrete pure component parameters are relaxed resulting in a hypothetical optimal working fluid and the corresponding optimal process. In a second step, real working fluids with similar properties are identified using Computer-aided Molecular Design and a second-order Taylor approximation of the objective function around the hypothetical optimum. So far, the process models in CoMT-CAMD were implemented in a procedural programming language, which hinders the reusability, the use for more complex processes and dynamic simulations. In this work, we integrate CoMT-CAMD into the object-oriented modelling language Modelica. For this purpose, Modelica is directly linked to PC-SAFT. Thereby, already existing model libraries for Modelica can be used to model the ORC process. The resulting design approach is applied to the integrated design of an ORC process and working fluid for a geothermal power station. 4th International Seminar on ORC Power Systems Energy Procedia, 129 ISSN:1876-6102

Applied thermal engineering 149, 1503-1521 (2019). doi:10.1016/j.applthermaleng.2018.07.078 Published by Elsevier Science, Amsterdam [u.a.]

Abstract Adsorption-based energy systems allow environmentally friendly heating and cooling by transforming solar or waste heat. However, solar and waste heat sources are often fluctuating over time. In this case, an optimal operation policy has to be identified. This operation policy has to consider the intrinsic dynamics of adsorption-based energy systems: the cyclic switching between adsorption and desorption phases. This work therefore presents a feedback-control strategy for adsorption-based energy systems. A two-step approach is employed: First, the original nonlinear dynamic process model is approximated by a set of linear ordinary differential equations (ODEs). In a second step, an optimization problem based on the linearized equations is formulated. The latter is solved approximately by a sequence of gradient steps. We apply the method to models for a solar cooling adsorption chiller and a solar collector. The method is shown to reliably provide optimal control and to be robust for a wide range of the investigated parameters.