Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started

Mirko Ginocchi, Ferdinanda Ponci and Antonello Monti
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Mirko Ginocchi: E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany
Ferdinanda Ponci: E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany
Antonello Monti: E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany

Energies, 2021, vol. 14, issue 24, 1-59

Abstract: Power systems are increasingly affected by various sources of uncertainty at all levels. The investigation of their effects thus becomes a critical challenge for their design and operation. Sensitivity Analysis (SA) can be instrumental for understanding the origins of system uncertainty, hence allowing for a robust and informed decision-making process under uncertainty. The SA value as a support tool for model-based inference is acknowledged; however, its potential is not fully realized yet within the power system community. This is due to an improper use of long-established SA practices, which sometimes prevent an in-depth model sensitivity investigation, as well as to partial communication between the SA community and the final users, ultimately hindering non-specialists’ awareness of the existence of effective strategies to tackle their own research questions. This paper aims at bridging the gap between SA and power systems via a threefold contribution: (i) a bibliometric study of the state-of-the-art SA to identify common practices in the power system modeling community; (ii) a getting started overview of the most widespread SA methods to support the SA user in the selection of the fittest SA method for a given power system application; (iii) a user-oriented general workflow to illustrate the implementation of SA best practices via a simple technical example.

Keywords: sensitivity analysis; global sensitivity analysis; local sensitivity analysis; variance-based sensitivity analysis; uncertainty analysis; Monte Carlo simulation; power system; bibliometric study; literature review; decision-making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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