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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