Data-driven representative day selection for investment decisions: a cost-oriented approach

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Title: Data-driven representative day selection for investment decisions: a cost-oriented approach
Authors: Sun, M
Teng, F
Zhang, X
Strbac, G
Pudjianto, D
Item Type: Journal Article
Abstract: Power system investment planning problems become intractable due to the vast variability that characterizes system operation and the increasing complexity of the optimization model to capture the characteristics of renewable energy sources (RES). In this context, making optimal investment decisions by considering every operating period is unrealistic and inefficient. The conventional solution to address this computational issue is to select a limited number of representative operating periods by clustering the input demand-generation patterns while preserving the key statistical features of the original population. However, for an investment model that contains highly complex nonlinear relationship between input data and optimal investment decisions, selecting representative periods by relying on only input data becomes inefficient. This paper proposes a novel investment costoriented representative day selection framework for large scale multi-spacial investment problems, which performs clustering directly based on the investment decisions for each generation technology at each location associated with each individual day. Additionally, dimensionality reduction is performed to ensure that the proposed method is feasible for large-scale power systems and high-resolution input data. The superior performance of the proposed method is demonstrated through a series of case studies with different levels of modeling complexity.
Issue Date: 11-Jan-2019
Date of Acceptance: 9-Jan-2019
ISSN: 0885-8950
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2925
End Page: 2936
Journal / Book Title: IEEE Transactions on Power Systems
Volume: 34
Issue: 4
Copyright Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/K002252/1
Keywords: Energy
0906 Electrical and Electronic Engineering
Publication Status: Published
Online Publication Date: 2019-01-11
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences

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