Motivation• “decarbonization” and higher performance of

the system are driving the ongoing revolution ofdistribution system

• “virtual power plant”, “super grid”,microgrids have been hypothesized for thesolution, among which the last is the mostpromising

• Conflict interests and uncertainties fromnot only technological perspectives but alsofrom regulatory necessitate the predictionof microgrid development

Results and findings• Summary of the four scenarios

ObjectivesTo identify different scenarios of futuremicrogrid development in the distributionsystem to shed light on microgrid research

Methods/Approach• A simple foresight method draws the scenarios into a

2*2 matrix considering two most criticaluncertainties—the Distribution system operator(DSO) and the customer

• Two axes of the quadrant are from passive customersto active ones, and from passive to active DSO,respectively

Conclusions• Three scenarios are identified and their use

cases, energy management system features, andmarket models are projected

• A collective effort from different parties isneeded for microgrid research to prepare betterfor the future

This work is funded by CINELDI - Centre for intelligent electricity distribution, an 8 year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20). The authors gratefully acknowledge the

financial support from the Research Council of Norway and the CINELDI partners.

Chendan Li, Olav Bjarte Fosso, Marta Molinas Jingpeng Yue Pietro RaboniNTNU, Norway Electric Power Research

Institute (China), ChinaENGIE EPS, Italy

Automatic network• Microgrid type: Non-isolated, mainly utility microgrid• EMS: The functions focus on applications benefit the utility Electricity• Electricity trading: Retail market, ancillary service market

Flexible and intelligent power system• Microgrid type: Large scale microgrid and microgrid clusters, system of systems(with nested microgrids)• EMS： the functionalities are based on data-sharing to coordinate the electricity production and usage via IoT• Electricity trading: Peer to Peer market, Ancillary service market, carbon trading market etc.

Passive network and passive customer

Distribution network as the backup:

• Microgrid type: Mainly self-owned/ customer-owned microgrid

• EMS: New way of keep power balance through peer to peer trading, etc.

• Electricity trading: Retail market, Peer to Peer market

Digitalized and autonomous network / active DSO

Passive DSO

Active customer

Passivecustomer

Dissemination No1. : Defining Three Distribution System Scenarios for Microgrid Applications in the 4th IEEE Conference on Energy Internet and Energy System Integration

Control and Operation of Microgrids for Smart Distribution System

Control and Operation of Microgrids for Smart Distribution System

Motivation and Objectives• Use data to model the grid which can be embedded it

into stability model

Results and findings

Methods/Approach

This work is funded by CINELDI - Centre for intelligent electricity distribution, an 8 year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20). The authors gratefully acknowledge the

financial support from the Research Council of Norway and the CINELDI partners.

Chendan Li, Marta Molinas, Olav Bjarte Fosso Nan Qin Lin ZhuNTNU, Norway Energinet, Denmark The University of Tennessee,

Knoxville, USA• Build a grid model that is high order and can

represent different operation conditions• Compatible with impedance stability analysis

Dissemination No2. : A Data-driven Approach to Grid Impedance Identification for Impedance-based Stability Analysis under Different Frequency Ranges

Presented in Powertech2019 DOI: 10.1109/PTC.2019.8810402

ai is the average distance from the vector to the other vectors in the same cluster as i , and bi is the minimum average distance from the vector i to vectors in a different cluster, minimized over clusters.