IEEE PES GM 2015Jesse GantzAlstom Grid

Integrating DER in DMS – Part 2Alstom Grid’s Technical Approach

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Agenda

• Integrated Distribution Management Systems• DER in IDMS• DER Modeling• Use Case 1: DER Operational Impact Studies• Use Case 2: AMI Meter as a Sensor• Use Case 3: Advanced Inverter Project with

NREL and Duke Energy

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Distribution Control Center (DCC) as a Human Brain

SCADA

DMS Core Functions

Outage and Switching

Network Analysis and Optimization

An Analogy

Nervous System

Vision and Memory

Logistics and Response

Reason and Decision Making

SCADA

ADMS

IDMS

OMS

DERMS

Integrated Distribution Management System

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DER in DMS - Requirements1. Provide visibility and coordination of

all distribution automation and DG

2. Seamlessly handle topology changes

3. Network analysis and optimization algorithms should take into account distributed generation

4. Provide basic forecasting (load/generation)

5. Provide analysis/decision support tools for switching, load shed, etc. in areas containing DGs

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1. Displays for viewing DG location, attributes. Full modeling and analysis of DG in DA schemes.

2. Real-time network topology processing across entire system.

3. Accurate modeling, incorporation in power flow, short circuit studies, VVO and reconfiguration applications

4. Support, but not require, various forecasting and look-ahead options:Static, imported, telemetry override.

5. Include DG in reconfiguration application, load shed, VVO apps, potentially as dispatchable resource

DER Modeling Challenges6

• Assets may be owned by the utility, an aggregator or the customer→ Some model data may be non-existent or difficult to obtain.

• DER are new and complex systems that data stewards and engineers may not fully understand → Inaccurate parameter capture

• DER technology and interconnection standards are changing rapidly → asset data schemas need to be extensible

• There are a variety of ways to model DER systems → Improper choice of model

Batt

PV

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Layers of DER Modeling7

Control

Telemetry

Injection

Equipment

• Local Controller Modes• Remote Control Capabilities• Eligibility for Optimization

• Source/Protocols• Values • Speed

• Forecast source• Power factor• Telemetry override?• Ratings• Device Model• Network Location and

Topology

Functional Prioritization8

Question Functionality Modeling Requirements

What, where and how big are they?

Display and Query

Location, type and basicattributes

Are they currently connected and affecting energization?

Topology Processing

Interconnection switch modeling, SCADA status, backfeed capability

What is it outputting and how is it affecting flows/voltages

NetworkAnalysis

Forecasted or telemeteredoutput, power systems parameters

Are they regulating or reactingto system state?

Autonomouscontrol

Modeling of control modes (i.e. volt-VAR, volt-Watt)

Can an operator control them? Manual remotecontrol

Mode control and setpointmodeling in SCADA

Can VVO, Load Shed or FISR control them?

Automatic remote control

Eligibility state parameters and available control modes

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Com

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• Example 1: 5MW Co-Gen with SCADA Recloser Interconnection• Equipment: Spinning machine model, step-up transformer, SCADA recloser/bypass• Injection: Driven by 3ph kW, kVAR SCADA values. Simple forecast if Suspect.• Telemetry: SCADA kW, kVAR, voltage, amps, utility-owned recloser state.• Control: Regulating to voltage setpoint. PV to PQ capability curve modeling.

• Example 2: Developer-owned 500kW Solar PV with standard inverter• Equipment: Energy resource object, no transformer, aggregate rating and limits• Injection: Dynamic forecast imported from DERMS system with site characterization.

Constant power factor based on interconnection agreement.• Telemetry: Irradiance or estimated irradiance used in DERMS forecast engine• Control: N/A

• Example 3: AMI Net-Metered Residential Home with Rooftop PV and Smart Voltage Control Device at Distribution Transformer

• Equipment: Distribution transformer, smart controller object, secondary voltage drop approximation, net load object with rating of PV/inverter, meter functions

• Injection: P, Q load based on load curves built from historical AMI data. Q from smart controller based on secondary voltage and setpoints.

• Telemetry: If available, AMI interface provides 5 minute interval data to override load curve. Queried on demand by power flow.

• Control: Advanced inverter object with local Volt-VAR mode. Q(V) setpoints modeled.

DER Modeling – Examples9

Use Case 1: Solar PV Impact Studies• IDMS Distribution Operators

Training Simulator (DOTS) used to analyze operational impacts of DG penetration

• A DG Modeler Tool adds hypothetical DG to the IDMS model

• Multiple time series simulations are performed and compared to identify salient results and deltas

• A Python Scripting Interface is used to fully automate the simulation, data capture and analysis

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Python for e-terradistributionPython Scripting Interface: A library that wraps the DMS Web Service API to provide simple functions in a scripting environment.Features:• Query data from in-memory database (DNOM)• Modify dynamics and manual entries• Call network analysis and optimization applications• Import historical measurements • Integrated tracing and topology analysis functions• Setup and perform time-series simulations• Simulation results can integrated with WebFG via iGrid

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Use Case 1: Solar PV Impact Studies• Study 1: System-wide impacts due to DG penetration

PNNL, “Duke Energy Photovoltaic Integration Study: Carolinas Service Areas”, PNNL-23226, Mar. 2014

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Use Case 2: AMI Meter as a Sensor

Courtesy of Itron

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Use Case 2: AMI Meter as a Sensor• Approach

• Implement near real-time interface between DMS and AMI system• Use AMI measurements to “override” demand/output forecast for difficult-

to-model C&I customers and solar installations 200-1000kW without SCADA

• Challenges• Latency: AMI data not synced with SCADA data

Configure meter for 5 minute interval. Assume constant kW/kVAR over interval: “Time-smearing”.

Throw out any measurements with sensed time data older than 5 minutes

• Comms network has limited bandwidth for non-billing purposes: Limit AMI measurements capture to fewer customers/DG Query data on demand prior to power flow solve once (~ 30 minutes):Trigger -> Contingency Read Request -> Wait -> Solve with available data

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Use Case 3: NREL - Duke Energy – Alstom Advanced Inverter Project

• Simulation Scope: 1 distribution feeder with a 5MW solar PV plant, 1 VAR-controlled switched cap, 1 fixed cap, 1 feeder head gang-operated regulator and 7 single phase line regulators. Time series simulations over 40 representative days in year at 1 minute time interval over daylight hours. Baselined with historical SCADA data. Simulation run over 26 different scenarios.

• Environment: Alstom e-terradistribution DOTS environment driven by Python Scripting Interface using Web Service API. Using Production DMS station model with minor updates. Used for both pure simulations and Power-Hardware-in-the-Loop (PHIL) testing.

• Control Schemes under Study:• “Local cap and taps”: Regulators, capacitor banks regulating in local control. Smart-inverter

connected DG running in fixed power factor mode.• “Local cap, taps and inverter control”: Smart inverter running autonomous control mode

using pre-configured settings and local sensing.• “IVVC caps and taps. Local inverter control”: Caps and taps running under IVVC (Integrated

Volt/VAR Control). Inverter regulating locally using autonomous control mode.• “IVVC caps, taps and inverter control”: Smart-inverter connected DER considered as

dispatchable resources in IVVC. VAR or PF set point control sent via IDMS/SCADA.

Use Case 3: NREL - Duke Energy – Alstom Advanced Inverter Project

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Full List of ScenariosScenario # System Model Cap and Tap

Behavior PV Plant Behavior

1 Validation Local Local, PF=Baseline2 Baseline Local Local, PF=Baseline3 Baseline Local Local, PF= 0.854 Baseline Local Local, PF= 0.905 Baseline Local Local, PF= 0.956 Baseline Local Local, PF= 0.987 Baseline Local Local, Volt-Var Mode 18 Baseline Local Local, Volt-VAR Mode 29 Baseline IVVC - Volt only Local, PF=Baseline

10 Baseline IVVC - Volt only IVVC, Q setpoint11 Baseline IVVC - Volt only IVVC, PF setpoint

12 Baseline IVVC - Min Demand Local, PF=Baseline13 Baseline IVVC - Min Demand IVVC, Q setpoint14 Baseline IVVC - Min Demand IVVC, PF setpoint15 Subdivision (100% PV) Local Local, PF=Baseline16 Subdivision (100% PV) Local Local, PF= selected 0.x17 Subdivision (100% PV) Local Local, Volt-Var Selected

18 Subdivision (100% PV) IVVC - Volt only Local, PF=Baseline19 Subdivision (100% PV) IVVC - Volt only IVVC, Selected Q or VAR20 Subdivision (100% PV) IVVC - Min Demand Local, PF=Baseline21 Subdivision (100% PV) IVVC - Min Demand IVVC, Selected Q or VAR22 2nd large PV at end of Feeder Local Local, PF=Baseline23 Large-scale PV Local Local, PF=Baseline24 Large-scale PV Local Local, PF= selectd 0.x25 Large-scale PV Local Local, Volt-Var Selected26 Large-scale PV IVVC - Volt only Local, PF=Baseline27 Large-scale PV IVVC - Volt only IVVC, Selected Q or VAR28 Large-scale PV IVVC - Min Demand Local, PF=Baseline29 Large-scale PV IVVC - Min Demand IVVC, Selected Q or VAR

Validation

Find “Best” Local Autonomous Mode

Find “Best” Centralized IVVC Mode

Add 100% PV Subdivision& compare selected modes

Maximize (large-scale) PV

Use Max PV & Compare selected modes

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