Conference programme

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Wednesday, 18 November 2015
11:30 - 13:00 Wind power supporting the grid
Integrating wind power into the electricity market  
Onshore      Offshore    


Room: Belleville

The aim of the session is to present advances on techniques that permit to wind farms to support the grid both in terms of frequency and voltage.  The ability to provide such services is one of the challenges today for increasing wind penetration. The session addresses also the potential of increasing wind penetration when considering smart grid problematics.

Learning objectives

  • Advances in techniques enabling wind turbines to comply with emerging grid codes for system services
  • Insight to the possibility offered by wind farms to contribute to frequency control
  • Advances in voltage management in grids with high wind penetration
  • How the control capabilities of wind farms can be used to mitigate any negative impacts on grid stability
  • How to increase wind penetration considering complementarity with demand with focus on electric vehicles
Lead Session Chair:
Alfredo Parres, ABB Group, Spain
George Kariniotakis, Professor, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES Paris Tech, France
Lina Maria Ruiz Gomez Maia Eolis, France
Co-authors:
Lina Ruiz (1) F Nicolas Girard (1) Sophie Guignard (1)
(1) Maia Eolis, Lyon, France

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Lina Maria Ruiz Gomez received her MSc. degree in Electrical Engineering from Grenoble Polytechnic Institute, Grenoble-INP, France in 2007. She received her PhD in Electrical Engineering from Grenoble-INP, in 2012. During her PhD, she worked on large-scale integration of wind power. From 2011 she worked at the Research and Development department of Maïa Eolis. Her research focuses on power systems, electricity market and wind power. She is currently involved in the standardisation group “Grid Code compliance assessment for grid connection of renewable power plants” within IEC.

Abstract

Improving the integration of wind power plants into the electrical networks: a new definition of the available power capacity

Introduction

The 2014 represented an improvement in the development of wind energy in France with 1042 MW of new wind power capacity added. It is expected that the contribution of electricity from wind farms will be increased in coming years. According to The French Energy Act, 19 GW of onshore wind capacity will be installed for 2020. The power available in most of the electrical substations is not enough to accommodate large amounts of wind farms. To achieve this energy goal, high investment in the infrastructures of electrical networks will be necessary to integrate wind power into the grid.
Nowadays, Transmission System Operator (TSO) and Distribution System Operator (DSO) keep designing electrical networks with a maximalist approach based on the nominal power of the power plant. However, as is well-known, the wind power is intermittent and wind turbines do not generate electricity at their rated power during the entire time of operation.
On the other hand, a new variable is integrating the French Energy Scenario. The plug-in electric vehicle (EV), such as electric cars (BEVs) and plug-in hybrids (PHEVs), are going to be developed in a large scale. For 2030, France must have deployed 7 Million of charging stations for EV. Once Vehicle to grid (V2G) is deployed, we need to consider a new parameter in the equation of Demand. In fact, the electricity consumption from EV would be representing about 2,5% of total French electricity consumption in 2030.
In this context, this work aims to develop a new dynamic method to integrate wind power in a limited power electrical substation with EV loads also deployed.

Approach

Our approach proposes to modify the method for calculating statics power flows. The classic approach of sizing electrical networks does not consider the behavior of the new power resources in planning study stage. In this work, we consider a dynamic load flow to calculate the availability of the network to integrate Wind power. This statistic method allows identifying critical operation conditions of the network when integrating the wind power and the EV loads in an electrical substation. This method also allows estimating the shortfall for the wind farm operator because of the limitation of power injected during normal operation. This shortfall could be integrated in the Wind Farm Operator Business Plan.




Main body of abstract

Many case studies are performed considering different scenarios of the annual electricity consumption and production in an electrical substation (with a seasonal effect).

Firstly, we define different scenarios of the electricity consumption in an electrical substation:
- demand curve with the french load profile
- real demand in a real electrical substation
- demand curve in an electrical substation calculated from DSO data (statistically defined load profiles)

Then our dynamic load flow is defined at the electrical substation point, which calculates for each timestep the total consumption and production from conventional power plants in the substation.
So, this result is modified (for every time step) by the stochastic behavior of the VE load.

The available injection power of the substation is then estimated and related to the wind farm.
Finally, the energy losses from the wind farm and the shortfall of the wind farm operator are estimated.


Conclusion

Currently, the development of wind power requires high investment for electrical networks expansion. A smart network sizing that controls the individual behavior of different consumers (including the new EV loads) and production sources would allow integrating much more wind power in the network while minimizing investments,
This research is the first step for a currently planned experiment in a French wind farm. It requires the installation of PLCs and measuring devices for exchanging information among producers, consumers, EV loads and the network operator.



Learning objectives
- To optimize sizing of the electrical network while integrating wind power into the grid
- Improving the use of available power capacity of the electrical network
- To quantify the shortfall of the wind farm operator (decision-making tool)