Bi-level fuzzy stochastic-robust model for flexibility valorizing of renewable networked microgrids

• Published in: Sustainable Energy, Grids and Networks Grids and Networks, 2022-09, Vol.31, p.100684, Article 100684
• Main Authors: Norouzi, Mohammadali, Aghaei, Jamshid, Niknam, Taher, Pirouzi, Sasan, Lehtonen, Matti
• Format: Article
• Language: English
• Subjects: Flexibility valorizing; Fuzzy decision-making; Hybrid stochastic-robust programming; Microgrid flexibility; Renewable networked microgrids
• ISSN: 2352-4677; 2352-4677
• DOI: 10.1016/j.segan.2022.100684

This paper presents a new bi-level multi-objective model to valorize the microgrid (MG) flexibility based on flexible power management system. It considers the presence of renewable and flexibility resources including demand response program (DRP), energy storage system and integrated unit of electric spring with electric vehicles (EVs) parking (IUEE). The proposed bi-level model in the upper-level maximizes expected flexibility resources profit subject to flexibility constraints. Also, in the lower level, minimizing MG energy cost and voltage deviation function based on the Pareto optimization technique is considered as the objective functions; it is bounded by the linearized AC optimal power flow constraints, renewable and flexibility resources limits, and the MG flexibility restrictions. In the following, the proposed bi-level model using Karush–Kuhn–Tucker (KKT) technique is converted to a single-level counterpart, and the fuzzy decision-making method is employed to achieve the best compromise solution. Further, hybrid stochastic-robust programming models uncertain parameters of the proposed model, so that stochastic programming models uncertainties associated with demand, energy price, and the maximum renewables active generation. Also, to capture the flexible potential capabilities of the IUEE, robust optimization models the EVs’ parameters uncertainty. Finally, numerical results confirm the proposed model could jointly improve operation, economic and flexibility conditions of the MG and turned it to a flexi-optimized-renewable MG. [Display omitted] •Bi-level model to value the microgrid flexibility is presented.•Integrated unit of electric spring with electric vehicles is modeled.•The paper deals with maximizing flexibility value in renewable microgrids.•Fuzzy stochastic/robust optimization is proposed to handle uncertainty.