Progressive hedging for stochastic energy management systems
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Progressive hedging for stochastic energy management systems The mixed-integer linear case Valentin Kaisermayer1,2 · Daniel Muschick1 · Martin Horn1,2 · Markus Gölles1,2 Received: 19 December 2019 / Accepted: 6 August 2020 © The Author(s) 2020
Abstract Energy systems have increased in complexity in the past years due to the everincreasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are
The research leading to these results was carried out in two research projects. One part of the research leading to these results received funding from the COMET program managed by the Austrian Research Promotion Agency under Grant Number 869341. The program is co-financed by the Republic of Austria and the Federal Provinces of Lower Austria, Styria and Vienna. The financial support by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development is gratefully acknowledged. The other part of the research leading to these results received was part of the Austrian flagship project ThermaFLEX managed by the FFG under Grant Number 868852 and funded by the Austrian Climate and Energy Fund in the framework of the research initiative “Green Energy Lab” as part of the Austrian innovation campaign “Vorzeigeregion Energie”. * Valentin Kaisermayer valentin.kaisermayer@best‑research.eu; [email protected] Daniel Muschick daniel.muschick@best‑research.eu Martin Horn [email protected]
Markus Gölles markus.goelles@best‑research.eu; [email protected]
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BEST - Bioenergy and Sustainable Technologies GmbH, Inffeldgasse 21/B, 8010 Graz, Austria
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Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21/B, 8010 Graz, Austria
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optimization-based EMS, which can for example be modelled as stochastic mixedinteger linear programmes (SMILP). Depending on the problem size and control horizon, obtaining solutions for these in real-time is a difficult task. The progressive hedging (PH) algorithm is a practical way for splitting a large problem into smaller sub problems and solving them iteratively, thus possibly reducing the solving time considerably. The idea of the PH algorithm is to aggregate the solutions of subproblems, where artificial costs have been added. These added costs enforce that the aggregated solutions become non-anticipative and are updated in every iteration of the algorithm. The algorithm is relatively simple to implement in practice, re-using almost all of a possibly existing deterministic implementations and can be easily parallelized. Although
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