4.8 Article

Distributed planning of electricity and natural gas networks and energy hubs

Journal

APPLIED ENERGY
Volume 282, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.116090

Keywords

ADMM; Distributed optimization; Energy hub; Planning; Unit commitment

Funding

  1. National Natural Science Foundation of China [51807115]
  2. Shanghai Sailing Program by the Science and Technology Commission of Shanghai Municipality [18YF1411400]
  3. Young Elite Scientists Sponsorship Program by the Chinese Society of Electrical Engineers [CSEE-YESS-2018016]

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The centralized planning assumption in the electricity network, natural gas network, and energy hubs neglects the independent ownership of different systems. A more practical approach is to establish a distributed planning framework that differentiates the electric system, gas system, and each EH as separate stakeholders. This paper proposes a distributed planning framework based on the ADMM, which decomposes the joint planning problem into multiple sub-problems for each system and EH, with unit commitment embedded for more accurate operation reflection.
Centralized planning of the electricity network, natural gas network and energy hubs (EHs) implicitly assumes a vertically integrated structure, which ignores the common independent ownership of different systems. A more practical approach should differentiate the electric system, gas system and each EH as separate stakeholders, and establish a distributed planning framework. Such a distributed planning framework based on the alternating direction method of multipliers (ADMM) is proposed in this paper, which uses the amount of electricity and natural gas required by EHs from each node as the decoupling information, and decomposes the joint planning problem into multiple planning sub-problems, respectively for the electric system, natural gas system and each EH. To reflect the operation more accurately in the planning stage, unit commitment is embedded in the planning model as its operation module. Finally, the proposed method is verified on an illustrative system composed of a modified IEEE RTS 24-bus electric system, Belgian 20-node natural gas system and four EHs. The case study demonstrates the impacts of unit commitment, gas price and penalty parameters on the planning scheme and the number of iterations.

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