Stochastic AC optimal power flow with approximate chance-constraints

Jeremias Schmidli; Line Roald; Spyros Chatzivasileiadis; Goran Andersson

doi:10.1109/PESGM.2016.7741699

Stochastic AC optimal power flow with approximate chance-constraints

Jeremias Schmidli, Line Roald, Spyros Chatzivasileiadis, Goran Andersson

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

With higher shares of fluctuating electricity generation from renewables, new operational planning methods to handle uncertainty from forecast errors and short-term fluctuations are required. In this paper, we formulate a probabilistic AC optimal power flow where the uncertainties are accounted for using chance constraints on line currents and voltage magnitudes. The chance constraints ensure that the probability of limit violations remain small, but require a tractable reformulation. To achieve this, an approximate, analytical reformulation of the chance constraints is developed based on linearization around the expected operating point and the assumption of normally distributed deviations. Further, an iterative solution approach is suggested, which allows for a straightforward adaption of the method based on any existing AC OPF implementation. We evaluate the performance of our method in a case study on the 24-bus IEEE RTS96 system. The proposed algorithm is found to converge fast and substantially reduce constraint violations.

Original language	English
Title of host publication	Proceedings of 2016 IEEE Power and Energy Society General Meeting
Publisher	IEEE
Publication date	2016
Pages	5 pp.
DOIs	https://doi.org/10.1109/PESGM.2016.7741699
Publication status	Published - 2016
Externally published	Yes
Event	2016 IEEE Power Engineering Society General Meeting - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016 https://ieeexplore.ieee.org/xpl/conhome/7593872/proceeding

Conference

Conference	2016 IEEE Power Engineering Society General Meeting
Country/Territory	United States
City	Boston
Period	17/07/2016 → 21/07/2016
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7593872/proceeding

Series	Ieee Power and Energy Society General Meeting
ISSN	1944-9933

Keywords

Power system planning and layout
Interpolation and function approximation (numerical analysis)
Power transmission, distribution and supply
Other topics in statistics
iterative methods
linearisation techniques
load flow
power generation planning
probability
stochastic processes
stochastic AC optimal power flow
chance-constraint approximation
electricity generation
operational planning method
short-term fluctuation
line current
voltage magnitude
limit violation probability
analytical reformulation
iterative solution approach
AC OPF implementation
24-bus IEEE RTS96 system
constraint violation reduction
Uncertainty
Reactive power
Wind power generation
Mathematical model
Generators
Wind forecasting
Load flow
Forecast Uncertainty
Chance Constrained Optimal Power Flow
AC Power Flow

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PESGM.2016.7741699

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Cite this

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title = "Stochastic AC optimal power flow with approximate chance-constraints",

abstract = "With higher shares of fluctuating electricity generation from renewables, new operational planning methods to handle uncertainty from forecast errors and short-term fluctuations are required. In this paper, we formulate a probabilistic AC optimal power flow where the uncertainties are accounted for using chance constraints on line currents and voltage magnitudes. The chance constraints ensure that the probability of limit violations remain small, but require a tractable reformulation. To achieve this, an approximate, analytical reformulation of the chance constraints is developed based on linearization around the expected operating point and the assumption of normally distributed deviations. Further, an iterative solution approach is suggested, which allows for a straightforward adaption of the method based on any existing AC OPF implementation. We evaluate the performance of our method in a case study on the 24-bus IEEE RTS96 system. The proposed algorithm is found to converge fast and substantially reduce constraint violations.",

keywords = "Power system planning and layout, Interpolation and function approximation (numerical analysis), Power transmission, distribution and supply, Other topics in statistics, iterative methods, linearisation techniques, load flow, power generation planning, probability, stochastic processes, stochastic AC optimal power flow, chance-constraint approximation, electricity generation, operational planning method, short-term fluctuation, line current, voltage magnitude, limit violation probability, analytical reformulation, iterative solution approach, AC OPF implementation, 24-bus IEEE RTS96 system, constraint violation reduction, Uncertainty, Reactive power, Wind power generation, Mathematical model, Generators, Wind forecasting, Load flow, Forecast Uncertainty, Chance Constrained Optimal Power Flow, AC Power Flow",

author = "Jeremias Schmidli and Line Roald and Spyros Chatzivasileiadis and Goran Andersson",

year = "2016",

doi = "10.1109/PESGM.2016.7741699",

language = "English",

series = "Ieee Power and Energy Society General Meeting",

pages = "5 pp.",

booktitle = "Proceedings of 2016 IEEE Power and Energy Society General Meeting",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE Power Engineering Society General Meeting, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

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Schmidli, J, Roald, L, Chatzivasileiadis, S & Andersson, G 2016, Stochastic AC optimal power flow with approximate chance-constraints. in Proceedings of 2016 IEEE Power and Energy Society General Meeting. IEEE, Ieee Power and Energy Society General Meeting, pp. 5 pp., 2016 IEEE Power Engineering Society General Meeting, Boston, Massachusetts, United States, 17/07/2016. https://doi.org/10.1109/PESGM.2016.7741699

Stochastic AC optimal power flow with approximate chance-constraints. / Schmidli, Jeremias; Roald, Line; Chatzivasileiadis, Spyros et al.
Proceedings of 2016 IEEE Power and Energy Society General Meeting. IEEE, 2016. p. 5 pp. (Ieee Power and Energy Society General Meeting).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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N2 - With higher shares of fluctuating electricity generation from renewables, new operational planning methods to handle uncertainty from forecast errors and short-term fluctuations are required. In this paper, we formulate a probabilistic AC optimal power flow where the uncertainties are accounted for using chance constraints on line currents and voltage magnitudes. The chance constraints ensure that the probability of limit violations remain small, but require a tractable reformulation. To achieve this, an approximate, analytical reformulation of the chance constraints is developed based on linearization around the expected operating point and the assumption of normally distributed deviations. Further, an iterative solution approach is suggested, which allows for a straightforward adaption of the method based on any existing AC OPF implementation. We evaluate the performance of our method in a case study on the 24-bus IEEE RTS96 system. The proposed algorithm is found to converge fast and substantially reduce constraint violations.

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PB - IEEE

T2 - 2016 IEEE Power Engineering Society General Meeting

Y2 - 17 July 2016 through 21 July 2016

ER -

Stochastic AC optimal power flow with approximate chance-constraints

Abstract

Conference

Keywords

UN SDGs

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