A Wasserstein distributionally robust planning model for renewable sources and energy storage systems under multiple uncertainties

Junkai Li, Zhengyang Xu, Hong Liu, Chengshan Wang, Liyong Wang, Chenghong Gu

Research output: Contribution to journalArticlepeer-review

Abstract

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and -norm Wasserstein metric constraints are considered to satisfy the decision-makers different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Sustainable Energy
DOIs
Publication statusE-pub ahead of print - 30 May 2022

Keywords

  • Carbon
  • Costs
  • decision maker's different preference
  • Decision making
  • Measurement
  • Planning
  • Renewable energy sources
  • Two-stage planning model
  • Uncertainty
  • Wasserstein metric
  • WDRO

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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