Abstract
We present an algorithm to solve capacity extension problems that frequently occur in energy system optimization models. Such models describe a system where certain components can be installed to reduce future costs and achieve carbon reduction goals; however, the choice of these components requires the solution of a computationally expensive combinatorial problem. In our proposed algorithm, we solve a sequence of linear programs that serve to tighten a budget—the maximum amount we are willing to spend towards reducing overall costs. Our proposal finds application in the general setting where optional investment decisions provide an enhanced portfolio over the original setting that maintains feasibility. We present computational results on two model classes, and demonstrate computational savings up to 96% on certain instances.
Highlights
1.1 BackgroundGovernments worldwide are pushing towards an increasing use of renewable energy technologies
Optimization models have a rich history for both operations of installed energy systems [1,33], as well as extending existing infrastructure; see, e.g., so-called capacity expansion models [2,25], models for integrating renewable sources with existing fossil fuels [29], and planning for expanding transmission networks [24]
The work in this article arises from a collaboration between mathematicians and energy-and-climate researchers at the Friedrich-Alexander-Universität Erlangen-Nürnberg and the Forschungszentrum Jülich, with the goal to improve the performance of the FINE package as well as other energy system models
Summary
Governments worldwide are pushing towards an increasing use of renewable energy technologies. Optimization models have a rich history for both operations of installed energy systems [1,33], as well as extending existing infrastructure; see, e.g., so-called capacity expansion models [2,25], models for integrating renewable sources with existing fossil fuels [29], and planning for expanding transmission networks [24]. The Framework for Integrated Energy System Assessment (FINE) is an open source python package that provides a framework for the modeling, optimization and analysis of energy systems [7,31] The goal of such optimization models is to minimize the total annual costs for deploying and operating energy supply infrastructure, subject to the technical and operational constraints of a multi-commodity flow energy-system problem.
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