Smart distribution systems multi-objective robust energy economic management of renewable-integrated smart distribution systems: Harmonic mitigation, voltage stability enhancement, and adaptive uncertainty management

Shang W., ƏLƏKBƏROV Ə., Xia B.

Energy Strategy Reviews, vol.65, 2026 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 65
  • Publication Date: 2026
  • Doi Number: 10.1016/j.esr.2026.102243
  • Journal Name: Energy Strategy Reviews
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Keywords: Energy sources, Renewable energy sources, Smart distribution networks, Solar photovoltaic, Voltage stability index
  • Azerbaijan State University of Economics (UNEC) Affiliated: Yes

Abstract

This paper develops a multi-objective optimisation model for Fexible Renewable-Integrated Energy Systems (FRIES) in Smart Distribution Networks (SDNs), which reduce operational costs, emissions, reduces harmonic distortion, and maximises voltage stability. Existing literature tends to address these goals separately. It does not account for the multiple uncertainties that coexist: renewable production, load, energy costs, and electric vehicle (EV) charging patterns. To fill this gap, this paper develops a linearised AC harmonic optimal power flow model with a symmetrical Voltage Stability Index (VSI) and uses Adaptive Robust Optimisation (ARO) with budget-bounded uncertainty sets to guarantee solution feasibility under real-world variability. The proposed framework is tested on a modified IEEE 33-bus test system over prediction error margins of 0% to 45%. Quantitative data shows that, at maximum uncertainty (45% prediction error), the proposed FRIES is able to achieve: a 55.2% decrease in operational cost (from $3547.8 to $1589.4), a 49.3% decrease in CO2 emissions (from 5628.4 to 2853.9 kg) a 48.6% decrease in the Total Harmonic Distortion (THD) of the voltages (incident to 6.07% to ARO approach ensures complete solution feasibility with computational tractability (45.6 s), which is appropriate in day-ahead operational planning of high-renewable-penetration distribution networks.