Project 3.4 Integrated 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

Static Volt/VAR Optimization Conservation Voltage Reduction Conventional Distribution Loss Reduction Methods Consumption Data Real-time V/I/PF Data Smart Meters Dynamic Voltage Optimization Dynamic VAR Reduction Volt/VAR Optimization Dynamic consumer Voltage Reduction Adaptive VRs and Transformer LTCs Conservation Voltage Reduction (CVR) Adaptive Real-Time CVR and Volt/VAR Optimization

Typical Electricity Distribution Network

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

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

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 Proposed Intelligent Agent-based VVO/CVR

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

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

IEC Goose Messaging Over PLC Goose Messaging Data Exchange between substation components Self-describing objects and functions (IEC ) 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.

Project Gaps and Challenges Design real- time VVO Engine (Algorithms) Data Aggregation, Management and Selection Optimal Agent Topology Database Architecture Have IEC Goose beyond Dist. Substation Bandwidth demand for the application layer PLC signal attenuation in step down distribution transformers

Questions? Thank You