Robust Multi-Objective Congestion Management in Distribution Network

Omniyah Gul M. Khan, Amr Youssef, Magdy Salama, Ehab El-Saadany

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Increased penetration of heavy loads is expected to lead to congestion in distribution networks. The distribution network operator can use Demand Side Management (DSM) to motivate consumers to shift their load from peak to off-peak times. In this paper, multi-objective optimization is utilized to schedule flexible load to alleviate potential congestions. The proposed scheme minimizes consumers' electricity cost and decreases the peak to average ratio of the load curve to a required level that alleviates existing congestion. This results in a consumer load schedule that is economical and does not require the imposition of congestion tariffs. However, the success of the proposed congestion management scheme relies on the accuracy of the consumer load consumption. Hence, in this paper, uncertainty analysis of consumers' flexible load schedule is executed to ensure the desired robustness of the power flowing in the distribution network to changes in uncertain variables. The results obtained are compared with the existing congestion management scheme demonstrating the advantage of the proposed multi-objective framework in terms of decreasing price and flattening the load curve while alleviating congestion.

Original languageBritish English
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Power Systems
StateAccepted/In press - 2022


  • Costs
  • Demand Side Management
  • Distribution networks
  • Filling
  • Load modeling
  • Loading
  • Multi-Objective
  • Pareto
  • price-based
  • Schedules
  • Uncertainty
  • valley-filling


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