Security region-based laminar flow coordinated optimization of grids. (January 2021)

• Record Type: Journal Article
• Authors: Yang, Tiankai; Yu, Yixin
• Is Part Of: International journal of electrical power & energy systems. Volume 124(2021)
• Publication Date: 2021-01
• Subjects: Power flow -- Coordinated optimization -- Layered decomposition -- Security region -- Hyperplane expression -- Karush-Kuhn-Tucker conditions; 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.2020.106406
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A Laminar Flow Coordinated Optimization based on layered decomposition is studied. An SSSR-based layered model is established to minimize the entire power supply cost. A coordinated algorithm based on KKT conditions is proposed for global optimality. The proposed method greatly increases computing speed compared with existing methods. Abstract: With the increasing penetration of renewable distributed generations in smart distribution grids, the coupling relationship between transmission and distribution grids has become increasingly complex. If power flow optimization for transmission and distribution grids is performed separately, the overall generation resources will not be fully utilized. In this paper, the power flow coordinated optimization based on layered decomposition is studied for the entire transmission and distribution grids, and the proposed method can be used on any scale grids. The core objective of this paper is the minimization of the overall supply cost. To this end, based on the steady-state security region (SSSR) in power injection spaces, the distributed local selfish optimization models of the transmission grid and each of the distribution grids is established, respectively. The coordination between transmission and distribution levels is achieved by using the Karush-Kuhn-Tucker (KKT) conditions. Due to the approximate hyperplane expressions of SSSR boundaries in power injection spaces, the operational constraints are transformed into a union of linear combinatorial inequalities of decision variables, and the variables in objective function and constraints are identical. Thus the optimization process and the determination of the KKT conditions become extremely simple, and the required number of iterations is very small. Several simulation results are presented, which show that the proposed method can accelerate the speed of solving problems by orders of magnitude.