Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic. (December 2021)

• Record Type: Journal Article
• Authors: Xu, Tao; Ren, Yi; Guo, Lingxu; Wang, Xiaoxue; Liang, Lemeng; Wu, Yuhan
• Is Part Of: International journal of electrical power & energy systems. Volume 133(2021)
• Publication Date: 2021-12
• Subjects: Active distribution networks -- Multi-objective robust optimization -- Distributed photovoltaic -- Uncertainty set; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107197
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A multi-objective robust optimization model for active distribution systems has been proposed. The range of the uncertainty set have been analyzed. Effectiveness, robustness and scalabilities have been fully compared. Abstract: The conventional single objective optimization cannot meet the requirements for the optimal operation of active distribution networks (ADNs) with multiple uncertainties brought by the distributed generation (DG), diverse demand responses and flexible topologies etc. In addition, the contradictions among multiple objectives, the tradeoff between the robustness, conservative, and deterioration of performance, as well as the computation cost have challenged the solution of multi-objective optimization, in particular for the ADNs with multiple uncertainties along with complicated operation scenarios. Considering the uncertainties of distributed photovoltaic (PV), a multi-objective robust optimization strategy for ADNs is proposed in this paper to maximize the utilization of renewables and to minimize the network losses at the same time. By constructing the uncertainty set of predicted PV's maximum outputs, the extreme scenarios of the uncertainty set are chosen to establish constraints, and reduce the dimension of calculation. Then, the multi-objective optimization algorithm can obtain the Pareto fronts under various ranges of uncertainty sets while satisfying the physical and operating constraints of the ADNs, the influence of various ranges of uncertainty set on the Pareto front is taken into consideration. The effectiveness and robustness of the proposed model are verified on a modified IEEE 33 bus system, and the scalability is tested on a 141 bus distribution network.