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Estimation of Power system frequency response based on measured & simulated frequencies
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0002-8308-5884
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0002-8189-2420
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0003-0471-9066
2016 (English)In: IEEE Power and Energy Society General Meeting, IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Electrical Power systems are going through a transition of increasing penetration of Renewable Energy Sources (RES) and growing transmission capacity between Asynchronous Areas (TBAA). Maintaining a reliable power balance is essential but most new RES and TBAA are not delivering Primary Frequency Controlled Reserves (PFCR) and not enhancing power systems Frequency Response Characteristics β. The issue addressed within this paper is to estimate β in this context. Accurate estimation is important for power system modelling or Automatic Secondary Reserve (ASR) design. We propose a method to estimate Frequency Response Characteristics based on measured and simulated frequencies. In this paper, we propose an iterative optimization method to obtain high resolution data from low resolution measurement. Based on the high resolution data, β is estimated with a σ approach. Then, we use linear regression to estimate the normal Frequency Containment Reserves (FCR). The proposed methods are tested in a Nordic Synchronous Power System case. Results show that our methods can give accurate estimations of frequency response characteristics and FCR.

Place, publisher, year, edition, pages
IEEE, 2016.
Keywords [en]
Frequency Bias Factor, Frequency Controlled Reserves, Frequency Response Characteristic, Nordic Power System, Electric power system control, Electric power systems, Electric power transmission, Frequency response, Iterative methods, Renewable energy resources, Electrical power system, Frequency bias, Power system frequencies, Renewable energy source, Transmission capacities, Frequency estimation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-202144DOI: 10.1109/PESGM.2016.7741498ISI: 000399937901140Scopus ID: 2-s2.0-85001764892ISBN: 9781509041688 (print)OAI: oai:DiVA.org:kth-202144DiVA, id: diva2:1081285
Conference
2016 IEEE Power and Energy Society General Meeting, PESGM 2016, 17 July 2016 through 21 July 2016
Note

QC 20170313

Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2018-01-16Bibliographically approved
In thesis
1. On Efficient Transmission Balancing Operation: Capturing the Normal State Frequency and Active Power Dynamics
Open this publication in new window or tab >>On Efficient Transmission Balancing Operation: Capturing the Normal State Frequency and Active Power Dynamics
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In an electric power system, there will always be an electric balance. Nevertheless, System Operators (SOs) often uses the term imbalance. Here, the term imbalance refers to the difference between trades and real-time measurements. This thesis defines the term imbalance and develops a framework helping SOs in finding better decisions controlling these imbalances. 

Imbalances are controlled by many decisions made at various stages before real-time. A decision can be to increase the flexibility in production and consumption. However, this is not the only decision affecting real-time balancing operation. Other decisions are grid code requirements, such as ramp rates of HVDC and generation; balancing market structure, such as imbalance fees and trading period lengths; and the strategies used in the system-operational dispatch.

The purpose of this thesis is to create a new possibility for SO to find decisions improving the balancing operation. 

In order to find and compare decisions, the thesis develops a framework that evaluates many different decisions made at various stages before real-time. The framework consists of the following. First, it develops an intra-hour model using multi-bidding zone data from a historical time-period; able to capture the normal state frequency and active power dynamics. The model creates high-resolution data from low-resolution measurements using several data-processing methods. The uncertainty from the historical time-period is re-created using many sub-models with different input data, time-scales and activation times of reserves. Secondly, the framework validates the model and identifies system parameters based on simulated frequencies and frequency measurements in the normal state operation. Finally; new decisions' are modelled, tested, and evaluated on their impact on selected targets supporting corporate missions of the SOs.

The goal of the framework is that it should be able to find better decisions for balancing operation but also that it should be applicable for real and large power systems. To verify this, the framework is tested on a synchronous area containing 11 bidding zones in northern Europe. Results show that the framework can be validated and trusted.

Three new decisions, made at various stages before real time, have been modelled, tested and evaluated. The modelled decisions were (i) lower ramp rates for generation, (ii) increased capacities for automatic reserves, and (iii) a new strategy for the system-operational dispatch. One implication of applying the balancing evaluation framework on data from July 2015 is that all tested decisions improve several selected targets supporting the corporate missions of the SOs. 

The conclusion is that the balancing framework is useful as a simulation tool in helping SOs in finding more efficient decisions for transmission system balancing operation.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018
Series
TRITA-EE, ISSN 1653-5146 ; 2017:161
Keywords
Active Power Dynamics, Frequency Control, Normal State, Power System Balancing, Transmission System Operator
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering Energy Systems
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-221221 (URN)978-91-7729-595-2 (ISBN)
Presentation
2018-02-07, L1, Drottning Kristinas väg 30, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20180116

Available from: 2018-01-16 Created: 2018-01-16 Last updated: 2018-01-16Bibliographically approved

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Publisher's full textScopushttp://www.pes-gm.org/2016/

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Söder, Lennart

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