OPAL-RT RT13: Real time simulation of distribution grids

© Fraunhofer IWES, Paris, 26.06.2013,

Real-Time Simulation of Distribution GridsPaul Kaufmann

Dr. J.-Chr. Toebermann

© Fraunhofer IWES, Paris, 26.06.2013,

Real-Time Simulation of Distribution Grids with high Penetration of Renewables and Distributed Generation

Dr. J.-C. Toebermann, D. Geibel, M. Hau, R. Brandl, P. Kaufmann, C. Ma, Prof. Dr. M. Braun, Dr. T. Degner


© Fraunhofer IWES, Paris, 26.06.2013, 3

The Fraunhofer-Gesellschaft in Germany

Fraunhofer-Gesellschaft, the largestorganization for applied research inEurope

undertakes applied research of direct utility to private and public enterprise and of wide benefit to society.• 80 research units, including

60 Fraunhofer Institutes• 20, 000 staff• € 1.8 billion annual research budget

Research centers and representative offices in Europe, USA, Asia and in the Middle East.



Fraunhofer IWES:Institute for Wind Energy and Energy System Technology

Research spectrum: Wind energy from material development to grid connection Energy system technology for all renewables

Foundation: 2009 Staff: approx. 500Annual budget: approx. 30 million eurosDirectors: Prof. Dr. Andreas Reuter, Prof. Dr. Clemens Hoffmann

Formerly: Fraunhofer-Center für Windenergie und Meerestechnik CWMT in Bremerhaven Institut für Solare Energieversorgungstechnik ISET in Kassel



Fraunhofer IWES: Business Fields

Environmental analysis for wind and ocean energy

Control and integration of decentralized converters

Energy and grid management

Energy supply structures and systems analysis



Renewable and Distributed Generation in Germany

Share of Renewable Energies in Power Supply Today: 23% 2020: 35% 2030: 50% 2050: 80%

Renewable Energies in Distribution Grids 45 GW capacity in LV and MV grids Reverse power flows -

example LV grid “Sonderbuch”Max. load: 130 KWp Max. feed-in: 1,200 KWp

Estimation of 42.5 billion Euros fordistribution grid reinforcement up to 2030

Sources: J. Appen, M. Braun, T. Stetz, K. Diwold, D. Geibel, “Time in the Sun”, IEEE Power & Energy Mag., vol.11, pp.55-64, March 2013



Project 1: Holistic Smart Distribution Grid Simulation

minimize charging costs improve integration of renewables reduce network extension costs

challenge: What are the behavioral and electric interdependencies and the dynamics within a distribution network?

intelligent generators and loads exhibit complex behaviors depend on local and global events and

decisions follow different and sometimes

contradicting goals example: charging of electric vehicles



Project 1: Holistic Smart Distribution Grid SimulationExample: Integration of Electric Vehicles

our vision: a system for HIL simulation and testing of system operation control strategies power HIL simulation of electric vehicles


Funded by BMU, 0325402


Project 2: Test Bench for System Stability based on Distributed Generation

Factors of influence of network stability

Balance between production and consumption

Coming inverter-dominated areas

Balance more complex

Compensation of system stability necessary

Overview of stability issues

Ron Brandl, Dominik Geibel


Overview of stability issues 2

Classic network stability

Prospective power plant change

New system stability necessary

Rotor angle stability

Frequency stability

Voltage stability

Compensation of conventional power plants stabilities regulation effect

require new stability functionalities of DER



Source: Definition and Classification of Power System Stability, IEEE/CIGRE Joint Task Force on Stability Terms and Definitions, IEEE Transactions on Power Systems, Prabha Kundur et. al.


State-of-the-art Network Simulator

Advanced PHiL Test Bench

Interaction between simulator and Device under Testing (DUT)

No feedback from device Feedback by current measurement as input for U/f calculation at network connection point

Voltage and frequency curves

- Fixed before test run- Independent from DUT

Depends on interaction between simulated network and DUT

Characteristics of network connection point

- Emulated by physical resistance- Network impedance is fixed

- Simulated - Adopted due to network behaviour

Power system capability

- Less complexity- No interaction between different functionalities

- Entire transmission/distribution networks- Influence of complex functionalities

Comparison between state-of-the-art and PHIL-Test-Benches




Simulation Model for Stability Analyses Transmission/Distribution networks

Prospective change of inverter dominated areas

3phase EMT model for PHIL simulation

>2500 nodes in transmission level and >100.000 nodes in distribution level




Transmission/Distribution networks

Prospective change of inverter dominated areas

3phase EMT model for PHIL simulation

>2500 nodes in transmission level and >100.000 nodes in distribution level

Development of distribution network equivalent

Simulation Model for Stability Analyses 2




Multi –Purpose Test Bench for Stability Research

Real-Time Simulation Transmission/Distribution level EMT signal output of defined

network buses Network fault

Generation unit Up to 300kVA Up to three units Rotation and static

DER Self-controllable Stability support

AcknowledgmentsWe acknowledge the support of our work by the German Ministry of Environment, Nature and Nuclear Safety and the Projekträger Jülich in the frame of the project “DEA-Stabil” (FKZ 0325585A).Only the authors are responsible for the content of the publication.




Project 3: Test Benches for Controllers of Wind Turbines / Wind Parks

HIL-Simulation and control for grid integration of Wind Energy

1. Wind park controller

Participation in grid voltage support (reactive power at PCC)

Active power control at PCC

2. Control on wind turbine level

Actuator of wind park controller (active / reactive power)

Fault-Ride-Through

Synthetic inertia, i.e. replicating the natural inertia in the grid

General aim: grid-friendly behavior = stable control system, avoid oscillations

Melanie Hau, Park Control and Real-Time Simulators


Development of Wind Park Controllers:

1. Software-in-the-loop (Basis: detailed model of the wind park / grid)

model insecurities (parameters, communication dead-times)

Software-only (hardware-related issues ignored, e.g. signal exchange)

2. Commissioning and testing in real wind parks

Testing for few, non-critical operating points

Environmental conditions (wind, grid) not reproducible

High costs

Additional hardware-in-the-loop testing prior to commissioning

Hardware controller and communication system connected to a real-time simulator of the wind park and superior grid

Systematic testing: reproducible, safe & cost-effective hardware testing




Basis: model library in Matlab/ Simulink Flexible with respect to real-time environment platform Embedded into automatic test processing environment


Funded by



Summary

Growth of wind and solar energy as well as rising E-Mobility usage will increasingly challenge grid assets, operation, and control

Real-time Hardware-in-the-loop simulation is essential for understanding the interdependencies and stability of "smart" inverters and system operation strategies and providing stability to the electric grid

The challenge of integrating "smart" grid components is urgent in Germany demanding for quick technical and regulatory solutions

Thank you very much for your attention

Contact: Paul Kaufmann / Dr. J.-Chr. [email protected] IWESKoenigstor 5934119 Kassel / Germany



Research Objectives and Real-Time Simulation

Primary objective of our research is to find solutions which are based on distributed power plants interfaced with an inverter lead to reduced / acceptable operational and investment cost ensure the current high quality standard in power supply

Application examples based on real-time simulation Simulation of distribution grid system operation Simulation of system stability based on distributed generation Simulation of grid connection of wind turbines and wind parks


OPAL-RT RT13: Real time simulation of distribution grids

Technology

fraunhofer iwes

fraunhofer institutes

wind energy

distribution network

ieee power energy

ocean energy control

research units

integration of renewables