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www.smart-microgrid.ca
Project 3.4Integrated Data Management and Portals
Dr. Hassan Farhangi, Dr. Ali Palizban, Dr. Mehrdad Saif, Dr. Siamak Arzanpour, Dr. Mehrdad Moallem and Dr. Daniel Lee
Students: Moein Manbachi, Maryam Nasri and Babak Shahabi
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• Static Volt/VAR Optimization• Conservation Voltage ReductionConventional Distribution
Loss Reduction Methods
• Consumption Data• Real-time V/I/PF Data Smart Meters
• Dynamic Voltage Optimization • Dynamic VAR ReductionVolt/VAR Optimization
• Dynamic consumer Voltage Reduction• Adaptive VRs and Transformer LTCsConservation Voltage
Reduction (CVR)
Adaptive Real-Time CVR and Volt/VAR
Optimization
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Typical Electricity Distribution Network
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Static (Pre-programmed and independent of real-time events)
Un-Intelligent (Absence of embedded device-level processing)
Independent Functions and constraints
Individual Volt/VAR device settings and control
Absence of system-wide visibility and monitoring
Absence of system-wide synchronization and coordination
Absence of automatic Fault Recognition and Restoration
Conventional VVO and CVR
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Opportunity: Could Smart Meters facilitate the evolution of Static VVO/CVR to Dynamic & Adaptive VVO/CVR?
Challenges: Management of massive amount of real-
time data generated by Smart Meters Dynamic, Adaptive & Cost-Effective
Volt/VAR Optimization Algorithms Distributed Command & Control Suitable Communication Protocols Data Base and Portal Architecture
Adaptive & Dynamic CVR &
Volt/VAR Optimization
Proposed Solution
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Real-time (On-Demand or Event Based)
Intelligent Agents (Multi-Agent Technologies)
Optimization Algorithm multi-O.F and multi-constraints
Dynamic control of Volt/VAR components
Reliable and Secure Communication Network
System-wide situational awareness
Pre-emptive/self healing Distribution Network
New ProposedIntelligent
Agent-based VVO/CVR
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Receive Data
Data from specific feeder
Data from neighboring Agents
Database
(Receiving Goose)
VVO Engine (VVOE)
Solving real-time VVO/CVR Objective Function based on dist. Network constraints
VVOE Algorithm
Send Commands
Re-configure distribution network
Optimize system operation
(Sending Goose)
Intelligent Agents
Downstream Agents
Control Center
Distribution Network
IEDs
Upstream Agents
Goose /other Protocols
VVO/CVR Intelligent Agent Primary Structure
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Real-time Data Processing
Need for peer-to-peer Messaging and Negotiations
Dynamic changes in load profile
Real-time operating system
Database structure for storage and mining of system wide data
Data aggregation and data filtering in nodes.
System Anatomy
No Central Supervisor
Different Types of data: INFORM, LEAKAGE, ALARM
Various smart components: Sensors, Smart-meters
Limitation of smart-meter memory size
Solution: Intelligent Agents
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IEC-61850 Goose Messaging Over PLC
Goose Messaging
• Data Exchange between substation components
• Self-describing objects and functions (IEC-61850)
Communication Structure
• PLC: Taking advantage of the existing media connecting distribution components
• Standard communication protocol between smart-meters and substation
Barriers
• PLC has a hostile medium with severe noise and attenuation
• PLC signal is attenuated significantly after crossing MV/LV transformer.
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Project Gaps and
Challenges
Design real-time VVO
Engine (Algorithms)
Data Aggregation, Management and Selection
Optimal Agent
Topology
Database Architecture
Have IEC 61850 Goose beyond Dist. Substation
Bandwidth demand for
the application
layer
PLC signal attenuation in
step down distribution transformers
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Questions?
Thank You