The conference center darmstadtium in Darmstadt is a prominent example of energy efficient buildings. Its heating system consists of different source and consumer circuits connected by a Zortström reservoir. Our goal was to reduce the energy costs of the system as much as possible. Therefore, we analyzed its supply circuits. The first step towards optimization is a complete examination of the system: 1) Compilation of an object list for the system, 2) collection of the characteristic curves of the components, and 3) measurement of the load profiles of the heat and volume‐flow demand. Instead of modifying the system manually and testing the solution by simulation, the second step was the creation of a global optimization program. The objective was to minimize the total energy costs for one year. We compare two different topologies and show opportunities for significant savings.
Abstract. Finding a good system topology with more than a handful of components is a highly non-trivial task. The system needs to be able to fulfil all expected load cases, but at the same time the components should interact in an energy-efficient way. An example for a system design problem is the layout of the drinking water supply of a residential building. It may be reasonable to choose a design of spatially distributed pumps which are connected by pipes in at least two dimensions. This leads to a large variety of possible system topologies. To solve such problems in a reasonable time frame, the nonlinear technical characteristics must be modelled as simple as possible, while still achieving a sufficiently good representation of reality. The aim of this paper is to compare the speed and reliability of a selection of leading mathematical programming solvers on a set of varying model formulations. This gives us empirical evidence on what combinations of model formulations and solver packages are the means of choice with the current state of the art.
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Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.
Booster stations can fulfill a varying pressure demand with high energy-efficiency, because individual pumps can be deactivated at smaller loads. Although this is a seemingly simple approach, it is not easy to decide precisely when to activate or deactivate pumps. Contemporary activation controls derive the switching points from the current volume flow through the system. However, it is not measured directly for various reasons. Instead, the controller estimates the flow based on other system properties. This causes further uncertainty for the switching decision. In this paper, we present a method to find a robust, yet energy-efficient activation strategy.
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