A novel chance constrained joint optimization method under uncertainties in distribution networks. (May 2023)

• Record Type: Journal Article
• Authors: Wang, Wei; Xu, Jinguang; Zhang, GuoWei; Yang, Ming; Xu, Xingming; Armghan, Hammad
• Is Part Of: International journal of electrical power & energy systems. Volume 147(2023)
• Publication Date: 2023-05
• Subjects: Joint optimization -- Capacitor control -- Network reconfiguration -- Chance constrained programming -- Kirchoff's law; 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.2022.108849
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A scenario based chance constrained programming model including Kirchhoff's law instead of power flow equations is established. The serious voltage and shunt current sources in Kirchhoff's law can be not only simultaneously optimized but also transformed into control devices. By the equivalent network transformation and basic loop equation, the network reconfiguration can be transformed into optimizing fictitious serious voltage sources in each basic loop instead of binary variables of open/close statue of switches. The solution space is massively reduced and the calculation efficiency is improved. A cluster of scenarios satisfying the desired confidence level in chance constrained formulations is formed for optimization by k-means algorithm. By optimizing this cluster, the chance constrained formulations can be automatically satisfied. The time consuming optimization of each scenario to find the feasible solution is avoided. A heuristic solution method which opens loops one by one to deal with radial topology constraint is proposed. This avoids introducing a lot of binary variables for the radial topology constraint as in existing analytic methods. Abstract: The joint optimization in distribution networks considering the uncertainties in wind power or photovoltaic (PV) outputs is a larger scale stochastic mixed integer nonlinear programming (MINLP) problem. However, how to efficient and accurate solve the problem under uncertainties is still a challenge. To handle the uncertainties, a scenario based chance constrained programming model is established. To improve the accuracy, the network reconfiguration and capacitor control are simultaneously performed by optimizing serious voltage and shunt current sources in the model by using equivalent network transformation. To improve calculation efficiency in dealing with chance constraints, a cluster of scenarios satisfying the confidence level is formed for optimization. To avoid introducing plenty of binary variables for the radial constraint and improving calculation efficiency, a heuristic method opening loops one by one is developed. The numerical simulations on distribution networks show the efficiency and accuracy of the proposed algorithm over the existing methods.