Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start

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Abstract

Energy System Models are complex MIPs that frequently need substantial CPU time to be solved. We study Capacity Expansion Models which are used for finding an optimal mix of technologies to secure stability in future energy systems. The need of model detail for this purpose tends to increase. Firstly, due to the many possible alternatives to fossil fuel, the solution space grows. Secondly, a detailed modelling including e.g. unit commitment makes the models NP-hard to solve. Consequently, simplification methods are needed. Promising results have been reported using aggregation, where the reduced models capture most of the needed investments while being much faster to solve. However, the solutions typically are sub-optimal, and a too aggressive aggregation may lead to infeasible solutions for the original problem. We analyze the potential of achieving optimal solutions using less computational time. By warm-starting the solution process of the original problem using solutions of aggregated problems, we
help the MIP solver in finding good search directions. Moreover, we suggest to exploit the high quality of aggregated solutions by reducing the solution space according to the aggregated investment strategy to reduce solution times even further. Results show that the gains of using warm starts depend on both warm starting solution and problem instance. Still, using the reduction span on investments, solution time reductions for most warm starts are seen with reductions up to 75%.
Original languageEnglish
Publication date2019
Publication statusPublished - 2019
Event30th European Conference On Operational Research
- University College Dublin, Dublin, Ireland
Duration: 23 Jun 201926 Jun 2019
Conference number: 30
https://www.euro2019dublin.com/

Conference

Conference30th European Conference On Operational Research
Number30
LocationUniversity College Dublin
CountryIreland
CityDublin
Period23/06/201926/06/2019
Internet address

Cite this

Buchholz, S., Gamst, M., & Pisinger, D. (2019). Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start. Abstract from 30th European Conference On Operational Research
, Dublin, Ireland.
Buchholz, Stefanie ; Gamst, Mette ; Pisinger, David. / Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start. Abstract from 30th European Conference On Operational Research
, Dublin, Ireland.
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title = "Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start",
abstract = "Energy System Models are complex MIPs that frequently need substantial CPU time to be solved. We study Capacity Expansion Models which are used for finding an optimal mix of technologies to secure stability in future energy systems. The need of model detail for this purpose tends to increase. Firstly, due to the many possible alternatives to fossil fuel, the solution space grows. Secondly, a detailed modelling including e.g. unit commitment makes the models NP-hard to solve. Consequently, simplification methods are needed. Promising results have been reported using aggregation, where the reduced models capture most of the needed investments while being much faster to solve. However, the solutions typically are sub-optimal, and a too aggressive aggregation may lead to infeasible solutions for the original problem. We analyze the potential of achieving optimal solutions using less computational time. By warm-starting the solution process of the original problem using solutions of aggregated problems, wehelp the MIP solver in finding good search directions. Moreover, we suggest to exploit the high quality of aggregated solutions by reducing the solution space according to the aggregated investment strategy to reduce solution times even further. Results show that the gains of using warm starts depend on both warm starting solution and problem instance. Still, using the reduction span on investments, solution time reductions for most warm starts are seen with reductions up to 75{\%}.",
author = "Stefanie Buchholz and Mette Gamst and David Pisinger",
year = "2019",
language = "English",
note = "30th European Conference On Operational Research<br/>, EURO 2019 ; Conference date: 23-06-2019 Through 26-06-2019",
url = "https://www.euro2019dublin.com/",

}

Buchholz, S, Gamst, M & Pisinger, D 2019, 'Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start' 30th European Conference On Operational Research
, Dublin, Ireland, 23/06/2019 - 26/06/2019, .

Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start. / Buchholz, Stefanie; Gamst, Mette; Pisinger, David.

2019. Abstract from 30th European Conference On Operational Research
, Dublin, Ireland.

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

TY - ABST

T1 - Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start

AU - Buchholz, Stefanie

AU - Gamst, Mette

AU - Pisinger, David

PY - 2019

Y1 - 2019

N2 - Energy System Models are complex MIPs that frequently need substantial CPU time to be solved. We study Capacity Expansion Models which are used for finding an optimal mix of technologies to secure stability in future energy systems. The need of model detail for this purpose tends to increase. Firstly, due to the many possible alternatives to fossil fuel, the solution space grows. Secondly, a detailed modelling including e.g. unit commitment makes the models NP-hard to solve. Consequently, simplification methods are needed. Promising results have been reported using aggregation, where the reduced models capture most of the needed investments while being much faster to solve. However, the solutions typically are sub-optimal, and a too aggressive aggregation may lead to infeasible solutions for the original problem. We analyze the potential of achieving optimal solutions using less computational time. By warm-starting the solution process of the original problem using solutions of aggregated problems, wehelp the MIP solver in finding good search directions. Moreover, we suggest to exploit the high quality of aggregated solutions by reducing the solution space according to the aggregated investment strategy to reduce solution times even further. Results show that the gains of using warm starts depend on both warm starting solution and problem instance. Still, using the reduction span on investments, solution time reductions for most warm starts are seen with reductions up to 75%.

AB - Energy System Models are complex MIPs that frequently need substantial CPU time to be solved. We study Capacity Expansion Models which are used for finding an optimal mix of technologies to secure stability in future energy systems. The need of model detail for this purpose tends to increase. Firstly, due to the many possible alternatives to fossil fuel, the solution space grows. Secondly, a detailed modelling including e.g. unit commitment makes the models NP-hard to solve. Consequently, simplification methods are needed. Promising results have been reported using aggregation, where the reduced models capture most of the needed investments while being much faster to solve. However, the solutions typically are sub-optimal, and a too aggressive aggregation may lead to infeasible solutions for the original problem. We analyze the potential of achieving optimal solutions using less computational time. By warm-starting the solution process of the original problem using solutions of aggregated problems, wehelp the MIP solver in finding good search directions. Moreover, we suggest to exploit the high quality of aggregated solutions by reducing the solution space according to the aggregated investment strategy to reduce solution times even further. Results show that the gains of using warm starts depend on both warm starting solution and problem instance. Still, using the reduction span on investments, solution time reductions for most warm starts are seen with reductions up to 75%.

M3 - Conference abstract for conference

ER -

Buchholz S, Gamst M, Pisinger D. Improving solution times of Capacity Expansion Energy System Models using aggregated problem solution as warm start. 2019. Abstract from 30th European Conference On Operational Research
, Dublin, Ireland.