LIST OF CORE-PIS AND PARTICIPATING MEMBERS
Hongbin Sun（孙宏斌）, Changjiang Scholar Professor, Tsinghua University
Liming Wang（王黎明）, Professor, Tsinghua University
Kameshwar Poolla, Cadence Distinguished Professor, UC Berkeley
Shmuel Oren, Earl J. Isaac Chair Professor, UC Berkeley
Scott Moura, Assistant Professor, UC Berkeley
VISION STATEMENT, GOALS AND IMPACT
L-SGRE’s mission is to foster research and educate students on fundamental theories and key technologies of building a green, efficient, reliable and optimized smart gridthat are crucial to the future of Shenzhen, the nation, and the world.
This Laboratory aims to become a world leading innovation center on following three areas:
Active distribution grids and distributed energy resources
AMI based load forecasting and robust estimation
Gain: Reduce need for reserves and understanding consumer behavior
Distributed autonomous optimal control for renewable resources and VAR compensators
Gain: Increase renewable energy penetration and reduce power loss
Gain: Improve network reliability
Distribution and transmission coordinated control and market design
Gain: improve overall renewable energy penetration and efficiency
Autonomous stability control for micro-grid
Gain: Keep micro-grid stable and efficiency
Coordinated dispatching of Energy Storage System
Gain: Improve its efficiency
Micro & main-grid coordinated control and market design
Gain: Improve overall renewable energy penetration and efficiency; Seeding an entrepreneurship ecosystem
Smart Buildings and Communities
Characterizing flexibility and controllability of smart buildings
Gain: Utilize flexible demand resources to support power grid operation
Comprehensive optimize and control of buildings, especial for HVAC
Gain: Improve energy efficiency, provision of frequency regulation services
Coordinated control of buildings in communities
Gain: Improve overall renewable energy penetration and efficiency
Characterizing DR and effects evaluation
Gain: Understand DR and its effects on power grid
Explore various services of DR
Gain: Use DR to improve the power grid operation
DR aggregator and market design
Gain: Management a large number of DR participation
Coordination between DR and power grid
Gain: Peak shaving, load shifting and involving ancillary services, frequency regulation e.g.
Integration of Electrical Vehicle to Grid
Wind-EV coordination on transmission grid side
Gain: Reduce carbon emissions on both transportation and electric grid sides
MPC-based load leveling with EV real-time charging
Gain: Improve economic of grid operation
ITS-based smart charging guide systems
Gain: Improve the security of the power grid and save drivers’ charging time
Infrastructure design and evaluation
Gain: Meeting physical power grid and cyber network’s requirements
Comprehensive modeling and simulation method for CPS
Gain: Characterizing and analyze the correlations between physical and cyber system.
Cyber contingency analysis and cyber-physical reliability
Gain: Identify the vulnerability of CPS, and generate enhancement strategies.
Big Data Based Knowledge Discovery for Smart Grid
Key features extraction &knowledge discovery based on operational data and exhaustive simulation for transmission systems
Distributed real-time simulation & sampling techniques
Gain: High efficient simulation platform for smart grid and automatically generate massive samples.
Knowledge discovery for operation schedule and preventive control
Gain: Enhance the security of large-scale power system and prevent cascading blackouts
Big data based knowledge management framework
Gain: Distributed storage, analysis and management, excellent scalability
Smart grid asset management and risk assessment based on online condition monitoring and PMU data
Condition data based state evaluation for power equipment
Gain: Evaluate working conditions of power equipment based on condition monitoring data
Outage model for power equipment
Gain: Describe the stochastic behavior and provide the availability of power equipment
Risk assessment based on outage model
Gain: Calculate the risk indicator and improve network reliability
Data-driven visualization, customer behavior analysis, low-carbon dispatch and incentive designs
Big data management platform for distribution grids
Gain: Multi-source heterogeneous data integration, storage, management and visualization
Load data analysis, correction and consumer behavior discovery
Gain: Improve the efficiency of demand-side management
Network configuration optimization and low-carbon dispatching based on big data
Gain: Improve network reliability, economy and security
Smart Grid Testbed
Develop a composite smart grid testbed for studying, education and demonstration include: (1)Electric power infrastructure composed of renewable generation, energy storage, grid and flexible loads;(2) Smart grid energy management and operation control platform including metering, communication, decision making & control system.
FIVE-YEAR RESEARCH PLAN, TIMELINES AND MILESTONES
Year 1 & 2
Research plan: Establish the research framework of the major strategic projects. The smart grid testbed will be preliminarily constructed. Some foresighted basic reach topics will be launched.
Milestones: Decentralized and coordinated energy management and operation control framework
Fully distributed autonomous control tools for active distribution networks
Research plan: Research on fundamental theory and key techniques for smart grid and renewable energy integration.
Milestones: Innovations on active distribution networks, distributed energy resources, and demand response. An energy management system for active distribution networks and its application in Shenzhen power grid.
Research plan: Research on key techniques for smart building & community and their onsite verification.
Milestones: Innovations on big data based knowledge discovery for smart grid, smart building and community and integration of Electrical Vehicle to Grid(V2G).A green & low carban demonstration park located in Shenzhen.
Research plan: The above key techniques verification, demonstration and deployment.
Milestones: Several world-leading achievements and demonstration projects for cutting edge technologies.
COLLABORATION AND MANAGEMENT PLAN
The Tsinghua and Berkeley co-PIs will have monthly conference calls to coordinate their research activities. We have started this collaboration through a PhD student Shen Yi from Tsinghua who is visiting Berkeley from March to October 2015.
COURSES THAT CO-PIS TEACH
•Fundamental of Smart Grids (2 Credits)
•Modern energy Management System (2 Credits)
•Data analytics for the Smart Grid (2 credits)
•Renewable integration in Electricity markets and Operations (2 credits)