Jianhui WangSection Lead – Advanced Grid ModelingCenter for Energy, Environmental, and Economic Systems Analysis (CEEESA)Energy Systems DivisionARGONNE NATIONAL LABORATORY9700 South Cass AvenueArgonne, IL 60439jianhui.wang@anl.gov; +1 630-252-1474http://ceeesa.es.anl.gov/https://sites.google.com/site/eejhwang/

Grid Modernization: Opportunities, Challenges, and Solutions

AEE SeminarFeb 28, 2017

About the speaker

Ph.D. Illinois Institute of Technology, 2007 Affiliate Professor, Auburn University (2011-) Adjunct Professor, University of Notre Dame (2014-) Senior Institute Fellow, Northwestern/Argonne Institute of Science and Engineering

(NAISE) at Northwestern University (2016-) Fellow, Computation Institute at The University of Chicago (2016-) Editor, Applied Energy, Journal of Energy Engineering, IEEE Power and Energy Magazine Editor-in-Chief, IEEE Transactions on Smart Grid Authored/co-authored over 200 journal articles and 50 conference publications and

7200+ citations. Recipient of the IEEE PES Power System Operation Committee Prize Paper Award in 2015. Secretary of the IEEE Power & Energy Society (PES) Power System Operations, Planning

& Economics committee and past chair of the IEEE PES Power System Operation Methods subcommittee Held visiting positions in Europe, Australia and Hong Kong including a VELUX Visiting

Professorship at the Technical University of Denmark (DTU) Technical program chair of the IEEE Innovative Smart Grid Technologies (ISGT)

conference 2012

Argonne is America's First National Laboratory and one of the World's Premier Research Centers Founded in 1943, designated a

national laboratory in 1946 Part of the U.S. Department of

Energy (DOE) laboratory complex– 17 DOE National Laboratories

Managed by UChicago Argonne, LLC– About 3,398 full-time employees– 4,000 facility users– About $760M budget– Main site: 1500-acre site in

Illinois, southwest of Chicago

Broad R&D portfolio and numerous sponsors in government and private sector Three Nobel Prize Laureates

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Grid Modernization and a Vision for the Future

Centralized generation Generation follows load One-directional power flow Limited automation Limited situational awareness Consumers lack data to manage

use Limited accessibility for new

producers

Centralized + distributed generation Variable resources Consumers become producers Multi-directional power flow Flexible load

4Source: ABB 2009; Texas Tech 2012

Grid Modernization – a $220 million DOE initiative

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My Version of the Future Power Grid

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Integrative Planning and operation (temporal), information technology and power engineering (cyber), integrated T&D (spatial), supply and demand (system-wide), power electronics devices

Interdependent coupling of critical infrastructures (e.g., gas, water, power, communications.)

Robust, Flexible and Resilient (e.g., uncertainty and variability of clean renewable energy, extreme weather events)

Hybrid Dynamic Control Architecturecentralized (e.g., HPC for faster-than-real-time interconnection-level simulation AND decentralized (e.g., microgrids, electric vehicles)

Cyber and Physical Secure

Source: DOE, 2011

Argonne Grid Research covers all time domains

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YEARS Long-term investment

dynamics Optimization and agent-

based investment modeling Climate change impacts on

future infrastructure

(SUB)SEC/MIN Dynamics modeling and

simulation Transient stability Optimal power flow Cascading failures

HOURS/DAYS Operational modeling, unit

commitment, economic dispatch, incl. stochastic modeling Power system restoration

Argonne grid research covers range of topics

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Variable solar and wind resource forecasting and integration

Short-and long-term resourceoptimization

– Thermal/hydro optimization– Energy storage– Stochastic short-term operations– Stochastic long-term investment/expansion Power market analysis

Smart-grid, cyber, and microgrid EV-grid interaction Building efficiency and building/grid

interaction Grid resilience to external events Climate change and environmental sciences Large-scale grid modeling using

high-performance computing

Argonne grid research designed to create value

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Advanced Algorithms•Predictive modeling•Advanced math/solvers•Scalable solutions for optimization•Integrative Frameworks

Model Development•Resource optimization•Stochastic UC/operations•Power market tools•Large-scale grid tools

Model Applications•Integration studies•Power market design•Long-term investment dynamics•Grid resilience, cascading failures power system restoration•Storage value/impacts•Climate change impacts

Deployment•EPFAST/NGFAST/POLFAST•HEADOUT, RESTORE, EGRIP•GTMax/ EMCAS/CHEERS•EZMT•AMP•onVCP/vBEOC

From Development of Advanced Algorithms and Models to Commercialization and Deployment

Renewable Energy Integration

Argonne developed new stochastic planning model to evaluate different operational practices and market policies to reduce the cost of wind integration Model is applied to case studies for Illinois (20% Wind)

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Reference: A. Botterud, Z. Zhou, J. Wang, R. Bessa, H. Keko, J. Mendes, J. Sumaili, V. Miranda, Use of Wind Power Forecasting in Operational Decisions, Argonne National Laboratory Technical Report, Sept. 2011, available at http://www.dis.anl.gov/pubs/71389.pdf.

A Resilient Self-Healing Cyber Security Framework for the Power Grid

Part

ners

Attack-resilient Wide-Area Monitoring, Protection, and Control (WAMPAC) framework

• Development of a self-healing Phasor Measurement Unit (PMU) network infrastructure through a risk mitigation model

• Development of bad data detection and attack-resiliency methods for the State Estimation (SE) algorithm

• Development of anomaly detection and attack-resilient control methods

Mitigation and prevention of cyber attacks

• PMU risk mitigation model employs optimal response to cyber-attacks with the goal of preventing the propagation of the attacks and maintaining the observability of the power system

• SE algorithm encompasses stealthy attack vector formulations, attack impact analysis, and Moving Target Defense (MTD) strategies to mitigate sophisticated cyber-attacks

Comprehensive anomaly detection, control, and resilience methods

• Model-based control algorithms leveraging CyberPhysical System (CPS) properties

• Wide-area protection schemes that include thedesign of a hierarchical model-based MTD-inspired protection algorithm leveraging spatial-temporal properties of device/system operation

• Model-based anomaly detection methods for the Optimal Power Flow (OPF) algorithm though the use of Principal Component Analysis (PCA)

Addresses Roadmap Milestones: 2.3, 3.4, 3.5, 4.5

Mobile Devices, Remote Access

Feeder Automation

Utility Central Operations

Field Area Network (FAN)

Energy Management System (EMS)

Other Utility’s Control Center

Home Area Network (HAN)

Wide Area Network (WAN)

Home Intelligence Substation Automation Transmission Automation

Distribution Automation (DA)

Cloud Computing

Local Area Network (LAN) Local Area Network (LAN)

Cyber-Physical Interface

Line Switchwith RadioTransceiver

Distribution Poles

AMICollector

Solar(or Wind)

SmartMeter

Electric Vehicles

SmartThermostat

CommunicationsTower

FiberOptic

Smart Substations(Transmission & Distribution)

BalancingAuthority

House

Generation

Cyber-physical Mapping of Attacks to WAMPAC

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• Energy Sector’s synthesis of energy delivery systems security challenges, R&D needs, and implementation milestones

• Provides strategic framework to– align activities to sector needs– coordinate public and private

programs– stimulate investments in energy

delivery systems security

Roadmap – Framework for Collaboration

Roadmap VisionBy 2020, resilient energy delivery systems are designed, installed, operated, and maintained to survive a cyber incident while sustaining critical functions.

For more information go to: www.controlsystemsroadmap.net

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A Resilient Self-Healing Cyber Security Framework for the Power Grid

The primary goal of the project is to develop an attack-resilient Wide-Area Monitoring, Protection, and Control (WAMPAC) framework, with associated computational algorithms and software tools, to prevent and mitigate cyber-attacks and achieve resilience of the bulk power system.

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New Cybersecurity Projects

A Resilient and Trustworthy Cloud and Outsourcing Security Framework for Power Grid Applications [Argonne (lead), Idaho National Laboratory, Illinois Institute of Technology, University at Buffalo, ComEd, PJM]– The primary goal of this effort is to develop a secure and trustworthy cloud computing and outsourcing

security framework for power grid applications, with associated computational algorithms and software tools, to prevent and mitigate cyber-attacks and achieve resilience of cloud-based power grid applications. The funding level is $2 million over the next five years.

A Tool to Ensure Uninterrupted Energy Flow from Cyber Attacks Targeting Essential Forecasting Data for Grid Operations [Brookhaven National Laboratory (lead), Argonne National Laboratory, Idaho National Laboratory, Orange & Rockland]– The goal of this effort is to develop a tool to ensure uninterrupted energy flow from cyber-attacks

targeting essential forecasting data for grid operations. The funding level is $3 million over the next five years.

University of Illinois Cyber Resilient Energy Delivery Consortium (CREDC)– A $28.1 million DOE award that will develop cyber-resilient energy delivery systems for the electric

power and oil & gas industries. The initiative is being led by the University of Illinois at Urbana-Champaign and includes 11 universities and national laboratories. The goals is to focus on improving the resilience and security of the cyber networks that serve as the backbone of the infrastructure that delivers energy to the nation, such as power grid, petroleum and natural gas pipeline systems.

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Cybersecurity for Renewables, Distributed Energy Resources, and Smart Inverters

In this project we will address the most critical cybersecurity vulnerabilities and threats for large-scale integration of distributed energy resources and smart inverters

Cybersecurity for Renewables, Distributed Energy Resources, and Smart Inverters

The primary goal of the project is to develop an attack-resilient architecture and layered cyber-physical solution to protect the critical power grid infrastructure and the integrated distributed energy resources (DER) from malicious cyber attacks across cyber, physical, and utility layers

New York State Reforming Energy Vision (REV) Initiative

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• Increasing requirements for grid resilience after Hurricane Sandy

• Distribution – level electricity market

• Diverse participants/resources (DR, DG, microgrids, storage, etc.)

• Distribution System Platform (DSP)

• Market platform and market rules

• Challenges exist – distribution OPF, market clearing, etc.

Argonne is supporting NY REV initiative

DOE FOA 997 - Research, Development, and Testing of a Master Controller with Applications to the Bronzeville Community Microgrid System

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DOE FOA 997 - Research, Development, and Testing of a Master Controller with Applications to the Bronzeville Community Microgrid System

Goal: Develop a Master Controller for a Community (McCom) with applications to the Bronzeville Community Microgrid (BCM), and develop an interconnected microgrid system design and test plan in the Chicago’s Bronzevillecommunity. Tasks: (1) a microgrid testbed; (2) autonomous

microgrid clusters; (3) cost/benefit analysis of alternatives for microgrids; (4) new reliability and power quality standards based on customer needs; (5) new rates and tariffs to correlate microgrids costs/benefits and customer payments; (6) terms and conditions of service for microgrid customers; (7) market mechanism; (8) service differentiation and rates Impact: McCom will help meet the DOE

targets: (1) 98% reduction in outage time of critical loads, (2) 20% reduction in emissions, and (3) 20% improvement in system energy efficiencies.

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Design, Implementation, and Testing Advanced Microgrids

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ComEd Community Microgrid in Bronzeville Chicago

Partnered with ComEd Co. to design and test the community microgrid in Bronzeville Chicago

Interactions between Multiple Microgrids and Distribution System

Impact analysis between multiple microgrid controllers and ADMS

Reconfigurable Microgrids to Mitigate the Influence of Natural Disasters

Implement dynamically reconfigurable microgrids to facilitate service restoration after disasters

Real-time Testbed for Testing Microgrid Controllers and Advanced Applications in DMS

Partnered with Eaton Co. to implement the real-time testbed based on OPAL-RT platform

Optimal Design Tool for Remote, Resilient and Reliable Microgrids

Partnered with LBNL and other national labs to develop optimal design tool for remote microgrids

DMS – Current Status and New Challenges

What is a DMS?

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Geographically correct map displays

Schematic One-LineDisplays

Graphical interface of DMS to display the physical connection of a regional distribution system

As accepted by the IEEE PES DMS Task Force,

“A DMS is a decision support system that is intended to assist the distributionsystem operators, engineers, technicians, managers and other personnel inmonitoring, controlling, and optimizing the performance of the electricdistribution system without jeopardizing the safety of the field workforce andthe general public and without jeopardizing the protection of electricdistribution assets.”

DMS – Current Status and New Challenges Distribution management system (DMS) plays a critical role in control and

management of distribution systems Increasing penetration of distributed energy sources (DERs) challenges the

conventional DMS Existing technologies for control and management of DERs include microgrid

controller (MC) and DER management system (DERMS) An advanced distribution management system (ADMS) should be developed with

the integration of MC and DERMS

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Microgrid - 3

AMI DRM OMS DMS DERMS

GIS SCADA Microgrid - 2MC - 1

Enterprise Integration Bus

Integrated approach to achieve an ADMS

DMS – Current Status and New Challenges

Current status of DMS development– Based on the information gathered from an industry survey, DMS has been widely

implemented in existing distribution systems– Only some of the DMS functions have been realized by utilities

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Status of DMS implementation

DMS applications being implemented

0 5 10 15 20 25

No Plans

Just Thinking

Planning

Procuring

Design and Test

Up and Running

Mid-Life Assessment

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

FLISRVVO

Online Power FlowSwitching Orders

Training SimulatorOutage Management

Tagging & PermitsIntelligent Alarming

Distribution State EstimationEngineering Analysis

Electronic Map UpdatesDER Management

Optimal Network ReconfigurationEmergent Load Shedding

Contingency AnalysisDemand Response Management

Load ForcastingEV Management

Asset Management

Source: IEEE industry survey, April 2015.

A DOE Initiative of $6 million/year for the next three years

Guidelines for ADMS implementation with Integrated DERMS & Microgrids Distribution management system (DMS) plays a critical role in control and management of

distribution systems Increasing penetration of distributed energy sources (DERs) challenges the conventional DMS Existing technologies for control

and management of DERsinclude microgrid controller(MC) and DER managementsystem (DERMS) An advanced distribution

management system (ADMS)should be developed with theintegration of MC and DERMS

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Integrated approach to achieve an ADMS

Guidelines for Implementing Advanced Distribution Management Systems –Requirements for DMS Integration with DERMS and Microgrids

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Chapter 1: Introduction Chapter 2: DMS – Current Status and New Challenges Chapter 3: Microgrid Operation Chapter 4: Distributed Energy Resources Chapter 5: Distributed Energy Resources Management Systems Chapter 6: DMS Integration with Microgrid Chapter 7: DMS Integration with DERMS Chapter 8: DMS Design Principles for Integration with DERMS and

Microgrids Chapter 9: Summary and conclusion

Upcoming Foundational DMS Report Series

1. Importance of DMS for Distribution Grid Modernization (ANL/ESD-15/16)

2. DMS Functions (ANL/ESD-15/17)

3. High-Level Use Cases for DMS (ANL/ESD-15/18)

4. Business Case Calculations for DMS (To Be Published)

5. Implementation Strategy for DMS (To Be Published)

6. DMS Integration of Microgrids and DER (To Be Published)

7. DMS Industry Survey (To Be Published)

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Follow-on projects:

• Interconnection, Integration and Interactive Impact Analysis of μGrids and the Distribution Systems

• Structuring a demonstration project to Integrate DER, MicrogridEMS, DERMS, and DMS

Impact Analysis of DERs Interconnected at the Distribution Level on the Bulk Energy System

Impact of DER Modeling for High-Level Analysis in BESDER Impact on Frequency Control of BESDER Impact on Voltage Control of BESDER Impact on Dynamic Stability of BESDER Impact on BES Planning and Operation StudiesGuidelines for Impact Analysis of DERs Interconnected at the

Distribution Level on the BES

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Structuring a demonstration project to Integrate DER, Microgrid EMS, DERMS, DMS Develop integrated control and

management systems fordistribution systems Address high penetrations of

interconnected DER Outcomes of the project will

include:– Gap analysis– Architectures and use cases

of integrated DMS system– Test plan and HIL Test setup @NREL– Proof-of-concept system integration

through modeling & simulation & HIT performance verification Testing

Site Demonstration and validation

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High-level Architecture with µEMS,DERMS, DMS

Demand Response of Smart Buildings

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DC Electrical Systems in Residential and Commercial Buildings

Implementation of a DC electrical system that is connected with rooftop PV arrays

Real-Time Control of Electrical and Thermal Energy Storage Resources in Buildings

Implementation of the dynamic controllers of electric vehicles and variable-speed heat pumps to provide demand response services and integrate renewable energy resources

Optimal Scheduling of Building Thermal Loads for Cost-Effective Power Balancing

Development of optimization problems to reduce electricity price and grid operating cost

Occupant-Centered Approach for HVAC System Control

Development of a building management system that is integrated with the sensing, control, and judgment of individual occupants on indoor temperatures

Measurement-Based Hierarchical Framework for Time-Varying Stochastic Load Modeling Development of a hierarchical load

modeling structure: Time-varying, stochastic, customer

behavior-driven and DR-enabled load models by leveraging practical utility data and laboratory experiments.

The proposed framework contains:1. representation of time-variant,

geographically-dependent, and stochastic load behaviors,

2. integration of DGs and power electronic loads,

3. consideration of uncertain customer behaviors, multiple DR programs, physics and economics,

4. application of PMU, SCADA and AMI data for real-time load model identification and calibration.

Argonne has broad energy Resilience Capabilities

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From Scenario Definition to System Restoration:EXAMPLE for Electric Power

Scenario Definition

•Describe plausible triggering event, such as weather/climate (hurricanes, ice storms, tornados), earthquakes, cyber, others

Physical Impact Assessment•Using fragility curves, assess physical damage to relevant infrastructure, including generators, towers/poles, wires, substations, fuel infrastructure (natural gas, coal, petroleum, etc.)

System Modeling•Model impact of loss of fueling infrastructure

•Model impact of loss of multiple grid assets

•Determine potential islanding and extent of blackout

System Restoration and Response Modeling•Physical restoration/repair time; crew scheduling/staging•Electrical restoration at transmission-level•Electrical restoration at distribution level•Response logistics

Argonne’s Comprehensive Set of resilience tools

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Prepare

Self-assessment(ERAP-D)

Emergency planning (onVCP/

SyncMatrix, SpecialPop, LPAT)

EP/PSR exercise/ drill (Scenarios, Threat-Damage, Impact Models)

Mitigate

Mitigation assessment

(EPfast, NGfast, POLfast)

Resource mitigation measures,

dependencies(IST-RMI)

Power system restoration

planning (EGRIP)

Blackstart resource planning (EGRIP)

Respond

Impact assessment (Threat-Damage, Impact Models)

Hurricane assessment (HEADOUT)

Emergency management/resp

onse (onVCP, vBEOC)

Recover

Real-time PSR analysis (EGRIP)

Emerge-Manage., Communication,

Collaboration (onVCP/vBEOC)

Argonne’s Energy Sector Resilience modeling tools EGRIP - Bulk Power System Restoration Operational restoration plans

– High-level plans with aggregations and approximations

– Not implementable

Solution refinement module – Take the start-up sequences produced by operations

optimization as input– Generate detailed restoration plans that are

implementable

– Refinement selects cranking path and determines generation dispatch levels to

– Satisfy technical feasibility such as voltage stability and transmission line thermal limit

– Pick up flexible loads to maintain stability during restoration

– Pick up loads after generators are started– Recalculate generator start-up time and critical load

pick-up time

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Sectionalization and Sequencing Optimization

Cranking Path Generation

Solution Repair

Compensation Device Addition

Cranking Path Modification

Startup Sequence Modification

Restoration Plan

Violated

Satisfied

Not

Sat

isfie

d, R

e-op

timize

Solution Refinement by SimulationTransient Stability Analysis and ACOPF

Frequency Overload Voltage Stability

No

Viol

atio

n

Argonne’s Energy Sector Resilience modeling tools

EGRIP - Advanced Distribution System Restoration– Data fusion module– Crew and resource dispatch module– Advanced control module– User-defined multi-objective optimization module

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Conclusions

Our electric power industry experiences unprecedented changes at a stunning pace

Significant supply side changes that include continued rapid growth of renewables and changes in our thermal power generation mix may lead to changes in markets, system operations, and system planning

New consumers and new technologies may alter our demand situation significantly and lead to more uncertainty in demand forecasts

Smart grid offers both opportunities and challenges

New approaches to data management, analysis, and modeling will be needed

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Contact info:

Jianhui WangEnergy Systems DivisionARGONNE NATIONAL LABORATORY9700 South Cass Avenue, Bldg. 202Argonne, IL 60439Tel: +1 630-252-1474jianhui.wang@ANL.gov