Distribution Grid Control and Optimization for High Penetration of Renewables

Distribution Overhead and Underground Operations and Maintenance Conference March 21-22, 2018, San Francisco, CA

Murali Baggu, PhDManager, Energy Systems Optimization and Control

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National Renewable Energy Laboratory

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• Solar Energy Research Institute (SERI) founded in 1977

• Designated as national laboratory in 1991

• World-class facilities and scientists

• 1,700 researchers, including more than 300 early-career scientists, students, and support staff

• National economic impact of $872 million annually

NREL is now celebrating its 40th anniversary

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NREL is a vibrant laboratory delivering

knowledge and innovation to

enable the transformation of our energy systems into a future that is

carbon neutral, highly efficient,

resilient, affordable, secure and

reliable.

Laboratory Vision NREL Innovation = Transformational Impact

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National Renewable Energy LaboratoryDistributed Energy Research and Test Facility (DERTF)

Field Test LaboratoryBuilding (FTLB)

Thermal Test Facility (TTF)

Main Campus

327 acres — Golden campus

305 acres — National Wind Technology Center

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• NREL’s largest R&D facility (182,500 ft2 /20,000 m2)

• Space for 200 NREL staff and research partners

• 15 state-of-the-art hardware laboratories

• Integrated megawatt-scale electrical, thermal and fuel infrastructure

• Peta-scale supercomputer and data analysis

• Interactive 3D advanced visualization

www.NREL.gov/ESIF

NREL’s Energy Systems Integration Facility (ESIF)

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Control, Optimization and Evaluation of Smart Grid Technologies Focus Areas

Control and Optimization Theory Distributed Control Strategies

Multi-Energy Systems

Autonomous Energy Grids

Distribution Automation Advanced Distribution Management Systems (ADMS)

Distribution system control and automation

Microgrid Controls/ Resiliency Controls

Smart Grid Evaluation Co-simulation of power systems, buildings and controls with power HIL

Building-level (residential/commercial) optimal control

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Real-time optimization and control of next-generation distribution infrastructure

Technical Approach

Develop a distributed optimization platform to enables distribution grids to emulate virtual power plants providing serviced to the main grid while (1) maximizing customers’ and utilities’ performance objectives and (2) ensuring that electrical limits are enforced.

Leverage: decomposability of optimization problems online optimization theory

Features

Distributed

Optimal and reliable

Real-time

Physics-based

Self-organizing

Establish: analytical results for stability Implement: algorithms in software/hardware

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Control Theory

Project objective: hierarchical optimization and control architecture for distribution feeders

- Power flow optimization (PFO): Slower time scale, intra-area, (stochastic) optimal power flow

- Aggregate device controller (ADC): real-time optimization and control of DERs, distributed

Formulate new classes of time-varying optimization problems and game theoretic formulations for ADCs

Develop innovative real-time distributed algorithms for optimization of ADCs

Established analytical performance analysis

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o Analyze system performance as the current inertia-dominated grid paradigm shifts to a future grid paradigm dominated by low-inertia inverter-based resources.

o Develop innovative grid-forming inverter controllers and characterize the system stability improvement.

Stabilizing the Power System with high penetration of DER

Stable

Unstable

“Tipping point” analysis of the IEEE 39-bus systemTo next-generation

grid-forming controlsFrom grid-following

controls

o Characterized the “tipping point” with traditional grid-following controllers, and investigated the impact of system components.

– Extensive study on the single-machine single-inverter case.

– Extended to multi-machine multi-inverter case.

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Scalable/Secure Cooperative Algorithms and Framework for Extremely-high Penetration Solar Integration (SolarExPert)

Design a modular, plug-and-play, and scalable Sustainable Grid Platform (SGP) for real-time operation and control of large-scale distribution networks (> 1 million nodes) Develop advanced

operation and control functions to manage extremely high penetration (> 100% peak load)solar generation in a cost-effective and reliable manner

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Distributed optimization and Control of Smart Multi-energy Districts

Objective:

Formulate new classes of optimization problems for coupled power, heat, and water systems

Develop computationally affordable solution approaches for non-convex problems

Develop distributed algorithms with various message-passing strategies

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Electrical-Thermal System Modeling Through Multi-Physics HIL Testing

Multi-energy HIL test capacity• Project impact

o Validate models of the fundamental physical coupling multiple systems through the electrical-thermal testbed with integrated HIL techniques, enhancing existing HIL capabilities and building the foundation for modeling, controller design, and optimization in multi-energy system area.

• Project objectiveso Provide a flexible, scalable, and controllable test capability for multi-energy

system.

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Key Features of AEGs

• Autonomous – Makes decisions without operators

• Resilient – Self-reconfiguring, cellular building blocks, able to operate with and without communications

• Secure – Incorporates cyber and physical security against threats

• Reliable and Affordable - Self optimizes for both economics and reliability

• Flexible – Able to accommodate energy in all forms including variable renewables

Autonomous Energy Grids (AEGs)

Challenges of the Future Grid• Going from hundreds to millions of controllable assets (both large-scale central station control and individual

microgrid control has been accomplished – but fully linking from large to small scales has not been done)• Increasing heterogeneous data and information from sensors• Unable to use current optimization techniques because of computational intractability• More interdependencies with communications and other energy domains (heat/cooling, gas, water,

transportation)AEG White Paper available at https://www.nrel.gov/docs/fy18osti/68712.pdf

optimized for secure, resilient and economic operations

Central-station based Grid

Microgrids

Nested, cellular control areas

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Optimization of Autonomous Energy Grids

Objective:

Formulate new classes of optimization problems for Autonomous Energy Grids

Develop computationally affordable solution approaches for non-convex problems associated with real-time operation of AEGs

Develop distributed algorithms for real-time optimization of AEGs with various message-passing

Autonomous energy grids (AEGs): scalable, reconfigurable, and self-organizing information and control infrastructure that promises extreme enhancements in terms of resiliency, security, and reliability

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Creating Autonomous Energy Grids – Basic Research Needs

OptimizationNonlinear Control

Big Data Analytics Complex Systems

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Smart Grid Devices

Cyber Security

Storage

Wind

Solar

Buildings

EVs

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AEG Workshop Sept 13-14, 2017Golden, CO

Report available at:https://www.nrel.gov/grid/autonomous-energy.html

Controls Theory• Develop scalable, real-time, decentralized and distributed controls that take

into account inherently asynchronous operations as a result of communications delays, losses, and distributed (asynchronous) control actions.

Optimization Theory• Develop computationally-affordable, stable, and provably optimal algorithms

that can be implemented in real-time and distributed fashions.

Complex Systems Theory• Develop modeling and simulation methods that address integration and

interdependencies of many different energy and communications systems at various temporal and spatial scales.

Big Data Analytics• Develop ways to use heterogeneous grid data (addressing access and privacy)

to better conduct ensemble forecasting of grid states and enable automated and distributed decision making from machine learning techniques.

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Autonomous Energy Grids

(AEGs)

Wind to AEGs

Integrating Foundational AEG Concepts Across Multiple Domains

Buildings to AEGs

Vehicle to AEGs

Solar to AEGs

Common Problem to be co-addressed:

Real-time controls and optimization

Hundreds to millions of control points

Asynchronous data and communications

Multi-domain systems (complex) and stochastic systems (variable renewables, consumer/occupant behavior)

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Distribution Automation

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ADMS Testbed Development

Project Description

• Model large scale distribution systems for evaluating ADMS applications

• Integrate distribution system hardware in ESIF for PHIL experimentation

• Develop advanced visualization capability for mock utility distribution system operator’s control room.

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ADMS Testbed Development

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ADMS Use Case 1: Schneider and Xcel Energy

Goal: Identify a trade-off between the depth

of remediation needed and the density of

measurements to implement various

advanced distribution management

applications like Fault Location, Isolation and

Service (Supply) Restoration (FLISR),

Integrated Volt-VAR Optimization (IVVO) and

Fault Location Prediction (FLP) on their system

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• Use Case 2: The ADMS Testbed team is actively pursuing a collaboration with Utilities and other DERMS vendors to identify a use case 2 targeted at improving the testbed capabilities to evaluate ADMS applications that integrate other utility management systems – Planed execution by April 2019

• Use Case 3: ENERGISE Eco-Idea project will use the ADMS tesbed to evaluate a novel Data Enhanced Hierarchical Architecture for Integrated operation of Centralized and Distributed voltage regulation schemes (Including centralized DMS operation, Autonomous or distributed Grid Edge devices and PV inerter operations) with high penetration of DER. – Planned execution by June 2019

• Use Case 4: PHIL Evaluation of Integrated system for Microgrid Energy Management System Integration with DMS - Interplay of µEMS, DERMS, and DMS/OMS via Field Verification. – Planned execution by November 2019

• Use Case 5: Evaluation of centralized and decentralized FLISR using Flexible DER and Microgrid Assets Enabled by OpenFMB – Planned execution by April 2020

ADMS Testbed Future Use Cases

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Enhanced Control, Optimization, and Integration of DistributedEnergy Applications (Eco-Idea)

Technology SummaryDevelop, validate, and deploy an innovative Data-Enhanced Hierarchical Control (DEHC) architecture that:- Comprehensively resolves the deficiencies of current operational

settings. - Enables an efficient, reliable, and secure operation of distribution

systems with massive penetration of solar energy.- Seamlessly integrates multiple voltage-regulation technologies to

achieve a reliable and efficient system-wide operation at multiple spatio-temporal scales in the face of volatile ambient conditions.

- As a first-of-its-kind deployment of the proposed DEHC platform, provides ample evidence of the effectiveness of the proposed approach.

Seamless system-wide, fast, and secure coordination among heterogeneous devices to achieve optimal and reliable operation of distribution systems with massive PV penetration.

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Open-Source ADMS Platform (GridAPPS-D)

Real-Time DER Setpoint Dispatch o Provide real-time DER set points

using distributed control Short-Term Grid Forecasting

o Forecast short-term loado Forecast distribution LMP

Solar Forecasting

NREL is developing three ADMS applications using GridAPPS-D:

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Open-Source ADMS Platform (GridAPPS-D)

An open-source tool to translate legacy system models into CIM format, as a supplement function of the GridAPPS-Dplatform: The Common Information Model (CIM) has become a widely accepted solution for information exchangeamong different platforms and applications. To address the challenge of most existing legacy systems that are not CIMcompatible,

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Impact: Real measured data as opposed to models will demonstrate how SDG&E service territory is currently and forecasted to be impacted for High Pen PV and EVs., applicable directly to other utilities.

Goal: Leverage existing AMI infrastructure to provide a foundational, pervasive secondary voltage monitoring network and a phase identification system, enabling >10%active devices to provide flexibility by 2035

Description: Data analysis optimized design for real-time controls and systems for high PV penetration in its service territory as well as determine the effectiveness of technology solutions such as energy storage, EV’s, smart inverters, flexible loads et cetera to mitigate any issue with High Pen PV. Leverage its existing AMI infrastructure to provide a foundational, pervasive secondary voltage monitoring network and a phase identification system.

San Diego Gas & Electric- Voltage Monitoring Analysis

ESIF Activity: ITRON AMI system will collect data from SDG&E and will then develop and propose algorithms through ESIF’s remote hardware in the loop (RHIL) for data analysis to be able to provide metrics back to SDG&E such as voltage regulation, fault location.

Project Team: SDG&E and Itron

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Holy Cross Energy: Enabling Distribution System Observability and Control for High DER

Impact: Pave the way to grid modernization of coops and municipal utilities, and enhance grid reliability and resilience.

Goal: Develop and validate new grid visualization, control paradigms, and business models for cooperativesand municipally-owned utilities through integration of grid-friendly intelligent DER assets

ESIF Activity:• Model HCE’s network and define use cases.• Evaluate advanced voltage regulation mechanisms

using hard-ware-in-the-loop (HIL) experiments.• Perform first-of-a-kind pilot field deployment• Analyze techno-economic cost-benefit of use cases.• Disseminate best practice through NRECA and NISC

Project Team: NREL, Holy Cross Energy, Survalent, NISC

Description: To solve a set of operational challenges faced by Holy Cross Energy (HCE) using new visualizationand control paradigms to enable wide-area situational awareness, active voltage regulation and power qualityenhancements. Outcomes from this project will enable new service offerings to HCE members, other co-ops andutilities, motivated by new validated business cases.

SCADA/AMI

Solar PV

Electric Vehicle

Flexible Building Loads

Legacy Voltage Ctrl Device

Visualization

Controllability

Business Model

Serious Voltage Problem

Lack of Obeservability/Con

trollabilityEconomics

Advanced Modeling and Techno-Economic

Study

ESIF HIL Test using ADMS Test Bed

Field Deployment

Distributed Control

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Eaton – Evaluating site control strategies for grid services

Goal: Optimizing mobility, solar, buildings and storage for grid services

Impact: Optimal integration of mobility with other DER technologies

Description: Electrification of transportation fleets provide an opportunity for optimizing multiple DER technologies. Synergistic site controls unlock additional value streams and accelerate technology adoption.

Technologies

Constraints Economics UtilityRevenues

Solar PV

Mobility

Energy Storage

Buildings

Project Economics

Optimal Operations

Controls Validation

HIL-based evaluation

REopt Analysis Transportation Use Profiles

Project Team

ESIF Activities • Detailed regional analysis for California, PJM and New York • FleetDNA data analysis to develop transportation and battery

use profiles• HIL evaluation for controls validation• Cost-benefit analysis

M

EVSE

BMS

PVEV

EVEVSE LoadStorage

Fleet Charging and Building Load Optimization

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Microgrid Controls/ Resiliency Controls

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CEC project: SDG&E’s Borrego Springs Microgrid

Borrego Springs, a desert community, served by one transmission line that extends 60 miles and is susceptible to severe weather and fires. • Expanding an existing small microgrid demonstration

project to the entire community of 2,800 customers.• A 26 MW PV plant that, along with substation and

community-scale batteries, and ultra-capacitors, enables the entire community to operate solely on renewable energy.

NREL Role: Evaluating how the microgrid performs using advanced microgrid controllers. Modeling and simulating the microgrid using real-time simulators at either NREL or SDG&E, connected to controller and power hardware at ESIF.

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• Power and Controller Hardware-in-the-Loop (PHIL & CHIL) evaluation of microgrid controller for Borrego Springs community microgrid site

ESIF role: CHIL/PHIL Testing

PV Simulator

PV Inverter

Power Hardware

Virtual Model

AC Source #1

69kV Substation Bus

ESIF RTDS

High Voltage

Low Voltage

12kV Substation Bus

Ckt 172Ckt 171Ckt 170

V and I Scaling

AC Source #2

V and I Scaling

Battery Simulator

ESS Inverter

Spirae Wave Commander

AC Bus #1AC Bus #2

DieselGenset

#1

NRG PV Model

SESS #2Model

SESS #1

NarrowsGrid Tie

MG Switch

V

)(1 tv

)(1 ti

A )(11 tik I

)(11 tvkV

Fictitious Bus #1

V

Fictitious Bus #2

A

)(22 tvkV

)(22 tik I

)(2 tv)(2 ti

G

Woodward EasyGen

Woodward EasyGen

RTAC

G

Controller Hardware

To RTDSTo RTDS

Communication

• CHIL: Spirae Wave microgridcontroller & EasyGen diesel generator controllers

• PHIL: ESS inverter (representative Schneider 540kW) & PV inverter (actual SMA 500kW)

• Remote HIL (RHIL): RSCAD network simulation at SDG&E’s ITF connected to hardware at NREL’s ESIF

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Microgrid Procurement Challenge

NREL hosted a dual-stage (CHIL, then PHIL) competitive event for microgrid control technology wherein contestants will compete on state-of-the-art test beds at NREL between June and December 2017. Stage 1: CHIL Evaluation + Cyber Review

Stage 2: PHIL Evaluation + Cyber Testing

Scoring of Key Performance Metrics* Resiliency and ReliabilityMicrogrid Survivability Power Quality Fuel- Free Asset Utilization Interconnection ContractUtility CommandsOperation and Maintenance

* NREL built upon KPPs developed at MIT Lincoln Laboratory. Relative weighting of KPPs derived from two focus groups held by NREL.

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Power Systems Testbed Schematic

Power-Hardware in-the-Loop (PHIL) Testbed at ESIF

Testbed Components:

• Microgrid controller – provided by participant• Real time power simulation – (RTS) Opal RT and

Mathworks - Matlab & Simulink• Operator interface (HMI ) and data manager-

SEL RTAC• Ametek 270kW bidirectional programmable AC

source/sink,• Research electrical distribution bus (REDB), • ABB 100kW solar inverter w/ MagnaPower

programmable DC source (solar array emulator),• Loadtec 250kW RLC load bank, • Caterpillar 250kW battery inverter w/ AV900

bidirectional programmable DC source/sink (battery emulator),

• Onan Cummins 80kW diesel genset w/ Woodward paralleling controller

• Nissan Leaf w/ electric vehicle service equipment (EVSE) and Sparkmeter

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Results- Microgrid Controller Innovation Challenge

• High external interest- potential customers need better information

• General controls- significant effort required to program “microgrid” controls

• Capabilities/functionalities tend to be overstated/understated

• Vendor participation resulted in new features being developed

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Smart Grid Evaluation

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Smart Grid Evaluation – Technology and Market Impacts

Co-simulation and performance analysis of power systems, buildings and controllers under different rate structures

Optimization of building (residential/commercial) operations based on Model Predictive Control (MPC)

Hardware-in-the-loop (HIL) simulation

KEY APPLICATIONS: System evaluation, including power system, buildings and appliances interacting with

control systems in the presence of different tariff structures

NREL developed a co-simulation platform (the IESM) to support smart grid evaluations under an LDRD and is applying that to studies of different control and market structures.

NREL developed Home Energy Management System (HEMS) algorithms under an LDRD and is developing an optimal dispatch controller for fuel cell-integrated commercial

buildings funded by DOE’s Fuel Cell Technology Office (FCTO)

NREL extended the IESM under an LDRD to include actual residential appliances through power HIL (PHIL), creating an HIL test bed for smart homes.

Tech-nology

Market Impact

• Building Thermal & Appliance Models

• Control System (HEMS)

Distribution Feeder Power Flow

Structured Tariff

Technologies save money and impact

consumption patterns

Technologies save money and impact

consumption patterns

Utility operations & finances are

impacted

Utility operations & finances are

impacted

Rates EvolveRates Evolve

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Integrated Energy System Model (IESM) co-simulation platform

• IESM simulates performance of technologies within multiple buildings under various retail marketstructures

• Co-simulation coordinator integrates feeder & building simulations, home energy management systems (HEMS) & markets

o Python-based

• HEMS schedules operation of appliances in response to consumer preferences, price, weather, and distributed generation forecasts

o Multi-objective, stochastic optimization based on model predictive control (MPC)

o HEMS controls thermostat, EVSE and water heatero Runs on HPC to parallellize hundreds of HEMS

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IESM simulation results with high HEMS penetration

• GMLC Future of Electric Regulation project, FY18: adding capability to evaluate 2-way (or export) rates - Implemented in several states and under consideration in others

• The Potential Impacts of a Highly Distributed Energy Environment project, funded by EERE’s Strategic Priorities and Impact Analysis group, will evaluate the impact of combining residential batteries and EVs with rooftop solar under HEMS control for both TOU and 2-way rates

• For the Transactive Energy Challenge hosted by NIST, incorporating network-level controls to coordinate HEMS behavior to meet feeder level objectives

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• Extended IESM co-simulation platform to include actual appliances through power HIL• This will accelerate and reduce cost of testing by combining large-scale software simulation with

hardware evaluation of a small set of representative systems

Smart Home HIL Test Bed

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Smart Home HIL Test Bed Results

• Simulation: o 13 node IEEE test feeder with 20 homeso Time-of-Use rateo Air conditioner, water heater and EV under HEMS control

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• Create an open-source tool set to foster growth in fuel cell integrated buildings with emphasis on optimal dispatch control

o Stationary fuel cells can be used for combined heat and power (CHP) to meet buildings’ electrical and thermal loads

o Transient response characteristics of fuel cells are evolving and they are capable of more dynamic performance

• The dispatch control objective is to minimize building operating costs, while maintaining occupant comfort

o Achieved by scheduling the operation of a fuel cell, storage and building demand using model predictive control (MPC)

o Responds to electricity tariff and ancillary services markets

• Will demonstrate with a co-simulation of the building in EnergyPlus and control in Matlab

o Year 3 will add an actual fuel cell through power HIL

Optimal Dispatch Control for Fuel Cell-Integrated Buildings

GUI: Main screen• Real time viewer of operation• Immediate user control over certain functions

GUI: Main screen• Real time viewer of operation• Immediate user control over certain functions

Thank you!Murali.Baggu@nrel.gov