Abstract
Decarbonization and defossilization of energy supply as well as increasing decentralization of energy generation necessitate the development of efficient strategies for design and operation of sector-coupled energy systems. Today, design and operation of process and energy systems rely on powerful numerical methods, in particular, optimization methods. The development of such methods benefits from reproducible benchmarks including transparent model equations and complete input data sets. However, to the authors’ best knowledge and with respect to design and optimal control of sector-coupled energy systems, there is a lack of available benchmarks. Hence, this article provides a model compendium, exemplary realistic data sets, as well as two case studies (i.e., optimization benchmarks) for an industrial/research campus in an open-source description. The compendium includes stationary, quasi-stationary, and dynamic models for typical components as well as linearization schemes relevant for optimization of design, operation, and control of sector-coupled energy systems.
Highlights
Realistic mathematical models of sector-coupled energy systems are a key for developing tailored numerical optimization methods, which in turn are essential for manifold research efforts towards a successful decarbonization, defossilization, and decentralization of energy supply
As a proof-of-concept as well as to show the wide range of applications of the provided model compendium, we propose two case studies as optimization benchmarks: (1) a bi-objective design optimization accounting for economic and environmental criteria based on all components of the generic energy system depicted in Fig. 1, and (2) a dynamic operational optimization of a sectorcoupled energy supply system with fixed, optimal design
We provide one model formulation on the scale of energy flow rates for each component considered
Summary
Realistic mathematical models of sector-coupled energy systems are a key for developing tailored numerical optimization methods, which in turn are essential for manifold research efforts towards a successful decarbonization, defossilization, and decentralization of energy supply. Numerical optimization allows to optimally plan, design, operate, and control energy systems while accounting for the inherent volatility of renewables as well as for environmental, economic, and social aspects, see Andiappan (2017); Mitsos et al (2018). Numerical optimization is in many cases the method of choice for control and automation problems (Engell, 2007; Engell and Harjunkoski, 2012; Kadam and Marquardt, 2007). Its importance for design optimization (Frangopoulos, 2018) and operation of energy systems is steadily increasing. The respective research efforts regarding the dynamic optimization of energy systems comprise a variety of methods and applications, spanning from the development of accurate and fast simulation methods for the control of thermal energy storage (Barz et al, 2018) via the incorporation of real-world weather forecasts (Constantinescu et al, 2011) to nonlinear modelpredictive control and/or real-time optimization of power grids with storage (Adeodu et al, 2019; Braun et al, 2018; Faulwasser and Engelmann, 2019; Matke et al, 2016), and the optimization of HVAC (Heating, Ventilation, and Air Conditioning) systems for buildings (Bürger et al, 2018; Harb et al, 2015; Perez et al, 2016; Touretzky and Baldea, 2016; Zhang et al, 2014)
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