4.8 Article

Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

期刊

APPLIED ENERGY
卷 292, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.116880

关键词

Mixed-integer linear optimization; Investment planning; Integrated energy systems; Climate policy; Facility location network design

资金

  1. European Institute of Innovation and Technology
  2. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]

向作者/读者索取更多资源

In order to achieve stringent greenhouse gas emission reductions, a transition of the entire energy system from fossil to renewable resources is needed. The main challenges of this energy transition are the carrier mismatch and temporal mismatch, which can be addressed by integrating multiple energy infrastructures. An optimization framework for long-term, multi-period investment planning of urban energy systems is proposed in this paper, aiming to support urban decision makers with long-term investment planning and addressing the challenges of the energy transition design strategy.
In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO2-emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

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