An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources. (15th February 2018)

• Record Type: Journal Article
• Authors: Sun, M.; Teng, F.; Konstantelos, I.; Strbac, G.
• Is Part Of: Energy. Volume 145(2018)
• Page Start: 871
• Page End: 885
• Publication Date: 2018-02-15
• Subjects: Clustering -- Transmission network expansion planning -- Resource variability -- Wind power; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.12.154
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Transmission Network Expansion Planning (TNEP) in modern electricity systems is carried out on a cost-benefit analysis basis; the planner identifies investments that maximize the social welfare. As the integration of Renewable Energy Sources (RES) increases, there is a real challenge to accurately capture the vast variability that characterizes system operation within a planning problem. Conventional approaches that rely on a large number of scenarios for representing the variability of operating points can quickly lead to computational issues. An alternative approach that is becoming increasingly necessary is to select representative scenarios from the original population via clustering techniques. However, direct clustering of operating points in the input domain may not capture characteristics which are important for investment decision-making. This paper presents a novel objective-based scenario selection framework for TNEP to obtain optimal investment decisions with a significantly reduced number of operating states. Different clustering frameworks, clustering variable s and clustering techniques are compared to determine the most appropriate approach. The superior performance of the proposed framework is demonstrated through a case study on a modified IEEE 118-bus system. Highlights: A novel objective-based scenario selection framework is proposed. Power flows and investment decisions are proposed as new clustering variables. Different clustering techniques are compared for scenario selection. The bi-level clustering on investment variables with K-means exhibits the best performance. The scheme results in large computation speedups at limited loss of accuracy.