Operation of coupled multi-owner district heating networks via distributed optimization

Kaisermayer, Valentin; Muschick, Daniel; Horn, Martin; Gölles, Markus

doi:10.1016/j.egyr.2021.08.145

Cited by 2 publications

(2 citation statements)

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“…To the best of the authors' knowledge, thus far, the field of distributed optimization of DHNs is barely researched. An approach for distributed optimization of DHNs based on the ADMM is presented in [23]. As far as we know, the literature does not provide any approaches applying OCD to DHNs.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Optimization of District Heating Networks Using Optimality Condition Decomposition

et al. 2022

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The optimal operation of District Heating Networks (DHNs) is a challenging task. Current or future optimal dispatch energy management systems attempt to optimize objectives, such as monetary cost minimization, emission reduction, or social welfare maximization. Typically, this requires highly nonlinear models and has a substantial computational cost, especially for large DHNs. Consequently, it is difficult to solve the resulting nonlinear programming problem in real time. In particular, as typical applications allow for no more than several minutes of computation time. However, a distributed optimization approach may provide real time performance. Thereby, the solution of the central optimization problem is obtained by solving a set of small-scale, coupled optimization problems in parallel. At runtime, information is exchanged between the small-scale problems during the iterative solution procedure. A well-known approach of this class of distributed optimization algorithms is Optimality Condition Decomposition (OCD). Important advantages of this approach are the low amount of information exchange needed between the small-scale problems and that it does not require the tuning of parameters, which can be challenging. However, the DHNs model equation structure brings along many difficulties that hamper the application of the OCD approach. Simulation results demonstrate the applicability range of the presented method.

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Section: Introductionmentioning

confidence: 99%

“…This is a deviation from the standard notation of the directed graph spanning up the DHN, where every edge is directed away from the start node and towards the end node. Still, the resulting system of equations is equivalent, as the differential pressure over the border edges ∆p e is defined using absolute values of A i,e ; see (23).…”

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confidence: 99%