A transmission expansion model for dynamic operation of flexible demand. (January 2021)

• Record Type: Journal Article
• Authors: Löschenbrand, Markus
• Is Part Of: International journal of electrical power & energy systems. Volume 124(2021)
• Publication Date: 2021-01
• Subjects: Robust optimization -- Transmission expansion planning -- Neural networks -- Demand response -- Renewable generation; 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.2020.106252
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A dynamic power flow model for flexible resources for dynamic operations & investments. An approximation of the operations problem to efficiently solve the investment problem. Shows how flexible demand can be a cost-effective alternative to grid investments. Abstract: This paper proposes a model to include investments in demand flexibility into traditional transmission expansion problems under uncertainty. To do so, a dynamic power flow model is proposed. The model is solved via applying a value function approximation in form of a neural network on the operational problem, allowing to yield a result for the non-convex investment problem. Additionally, robust sets are applied and linearized to deal with uncertainty and decrease computational complexity. In similar manner, Karush Kuhn Tucker conditions are used to transform a tri-level into a bi-level problem. Case studies for systems of varying complexity show the convergence of the algorithm as well as that flexible resources can be used as a cost-effective substitute for transmission lines in grid expansion.