"Dynamic Distribution System, a new Architecture for the

Integrated Grid"Bruce Beihoff

Tom JahnsBob Lasseter

University of Wisconsin – MadisonIEEE PES 2015 - Panel

July 29,2015

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Panel Presentation Abstract"Dynamic Distribution System, a new Architecture for the Integrated Grid"Abstract:For the first time in decades the Electrical Grid is undergoing great change. The advent of distributed energy resource systems (DERS) and large scale improvements in electrical power conversion have combined to become an engine of this change that extends far beyond the growing and measurable effects of just today. Almost universally this engine has driven us towards a rethinking of the Distribution Grid, a part of the Electrical Network that had remained for the most part constant for 70 years. In this talk we will walk down this path a bit ahead of the vision we see in today's technical journals towards a proposition we call Dynamic Distribution System. This architectural approach promises to help us rethink the grid "from the middle out". It holds out the possibility of grid evolution that increases speed fast enough to become a grid revolution. It holds out the possibility of an architecture that creates the best combination of the central , the distributed, the old, and the new in power systems. It holds out the further promise of forming new integrated value architecture with the fuel, water, and resource grids that have always been intertwined with the Electrical Grid and the society that counts on it. It is the second great network challenge of the next industrial revolution.

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Outline

• Background Dynamic Distribution• Dynamic Distribution System Principles• Architectural Approach to DDS• Challenges• Benefits• The Path Forward ..

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Background DDS

• Dynamic Distribution System is a Electrical Distribution Power System Architecture utilizing the best attributes of distributed and centralized power topologies.

• DDS utilizes a combination of the best capabilities of autonomous DER control (e.g. CERT’s

droop based control) and Hierarchal Control (Multi-grid, Model Predictive, Moving Horizon, ...)

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Background DDS

• To understand our proposition we will have todiscuss two major converging themes:– The Need for Dynamic Distribution System– Dynamic Distributed Power System Concepts– Architectural Concepts

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A Changing Grid Environment6

Pro’s, Con’s, Approaches7

Pros and Cons of Central and Distributed Power

Ref: [1]

Solution, Move SomeResources to Distribution Grid...

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Ref: [1]

Dynamic Distribution Concept

M-WERC DDS MG2 9.11.2014.7

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Personal Power PlantsDynamic Distribution SystemCentralized Grid

Centralized Decentralized

Least Autonomous Most Autonomous

Best of Centralized Grid Best of Personal Power PlantsRef: [1]

What do the Icons Mean ?

M-WERC DDS MG2 9.11.2014.7

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Centralized

Least Autonomous Most Autonomous

Personal Power PlantsDynamic Distribution SystemCentralized Grid

Best of Centralized Grid Best of Personal Power Plants

Bulk Generation

Transmission

Load

Local Generation

Distribution

Price Signal

Marketplace

Controller

Electricity

CommunicationRef: [1]

Smart Feeders/Microgrids ?

M-WERC DDS MG2 9.11.2014.7

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Personal Power PlantsDynamic Distribution System

Centralized Grid

Centralized Decentralized

Least Autonomous Most Autonomous

Feeders (& u-grids)

Feeders (& u-grids)

Feeders (&

u-grids)

Best Path for Next Gen Distribution Architectures

Dynamic Distribution Concepts12

GL

SL

S/S

L

C/C

L

Subst

LLLG

GC/C

S

Subst

L

S

Cluster Controller Distributed

Storage

Cluster 1(Functional

Feeder)

Cluster n

Load

Substation SmartSwitch

Distributed GenerationSS-MSC

Substation Midscale Controller

Power Network(Feeder)

Control Network

HSC(DER)High Scale Controller (Multiple

Sub-Stations)

Cluster (Functional Feeder) Controller

Dynamic Distribution Concepts13

t

interface

Switch Gear

Protection

interface

“Grid forming”Generation & Storage Autonomous Merchant DER

interface

Static Switch,Feeder

Automation

interface

High Scale Control (HSC) (minutes- hours)• Large Power Flows & Protection Load Tracking

Voltage /Frequency• Volatility Minimization• Areas CHP ,Renewables• Performance/Price Optimization/Market Models

Mid-Scale Control (MSC) (Sub Station)(seconds-minutes) •Mid-Scale Power Flow•Multiple Cluster/Feeder Coordination

High Scale Controller

Utility RequestAvailable Ancillary services

“Midscale Grid following/forming” Generation & Storage

CC Function Priority• Fast Dynamics• Short Circuit• ESD Resilience• Intrinsic Security

MSC Function Priority• Mid-Scale Power Flows• Combined Effects

on diverse Networks• Market Signals

HSC Function Priority• Combined Effects

on larger diverseNetworks

• Large Flow Optimization• Market Interactions

Mid Scale Controller

DSODSO

TSO

DSO

T/D T/D T/D

Control System Functional Layers(Hierarchal Control)

Loads

Clusters Control (CC) & Autonomous Layer (10-1000 milliseconds)• Track loads, regulates voltage, frequency,

reactive power, and provide local stability• Fastest Protection, Flow & Load Control• Autonomous Resilience

Cluster ( Functional Feeder) Controller

Pon off

P, Von off

statemode

statemode

Mid Scale Controller

Mid Scale Controller

High Scale Controller

High Scale Controller

Translating Layers to the Iconograph of DDS

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Dynamic Distribution Concepts15

T/D INTERFACE

SUBST 1;1 SUBST 1;2 SUBST 1;N

SUBST 2;1

SUBST 2;2

SUBST 2;3

SUBST 2;4

...

...

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SS-MSC

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C/C[ ] [ ]

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C/C[ ] [ ]

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C/C[ ] [ ]

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C/C[ ] [ ]

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C/C[ ] [ ]

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C/C[ ] [ ]

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C/C

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C/C[ ] [ ]

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C/C

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SS-MSC SS-MSC SS-MSC

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HSC (DER)

...

FUNCTIONAL CLUSTERS (FEEDERS WITH CONTROLLERS)

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SS-MSCSS-MSCSS-MSC

Distribution Grid Equipment Map

Source: Wiki Images DDS Algorithm Family can run on Grid Automation Hardware Platforms

SL

GL

S/S

L

C/C

L

Subst

Cluster Controller Distributed

GenerationLoad

Substation SmartSwitch

Distributed Storage

Power Network(Feeder)

Control Network

Dynamic Distribution Concepts16

Many Tests/Models Demonstrate ClusterFeeder Control

AUTONOMOUS DER SOURCEPOWER BALANCING

Ref: [1]

DDS and Hierarchal Control(Feasible Cooperation – Model Predictive Control, Multi-Grid Formulation, Moving Horizon

Prediction)

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Functional Clusters (Basic Unit)

Cluster Control Level

Mid-scale Control ( Substations)

High Scale Control (HSC) Level

High-Level Distribution (Areas)Multiple sub-stations

Aggregated to Distribution Areas

Multiple Clusters Aggregated to Sub-Stations

Course Time ScaleLong Predictive Horizon

Mid Time ScaleMid Predictive Horizon

Short Time ScaleShortest Predictive Horizon

“Reduction of required States forOptimum Control”

Feasible Cooperation Model Predictive Control(FC-MPC ) Multigrid Formulation

Standard AGC for an MSC –CC GroupLarge Disturbance

Ref: [4],[5], [9]

DDS Hierarchal Control And Fault Protection

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DDS : Architecture Defined A logical description of present and future interactions between structure and

function .... (Natures View)

A logical description of interactions between structure and function to meet present and future objectives.. (Designers View)

A set of principles that enable interactions between structure and function to meet present and future objectives.. ( Framework View)

S1 S2 S3 S4 S5

F1

F2

F3

F4

F5

Structure

Func

tion

Gen II Gen III

ARCHITECTURE AND THE GRIDConceptual Functions and Structures Primary Dynamic Network

Multi Doman Networks Domain Relations Standards

The Journey

Microgrids

Distribution Grid

Transmission Grid

Ref Source: Siemens Whitepaper 2013

Ref: WIKI Commons License Various

Source: NIST Smart Grid Framework v1.0 2010

Source: NIST Smart Grid Framework v1.0 2010

The Grid may be the Biggest System

Electrical Grid

Fuel Grid

Water Grid

Atmosphere Grid

Economic Grid

• Coupling is increasing between these grids ....• The Largest Man Made Systems interacting with the Largest Terrestrial Systems• Could we hope to improve our grids without Architecture ...?

DDS Architecture : You don’t have to start big ....

• You can begin the Architectural Evolution at key gating application cases .....

A Typical Design Processfor DDS Applications

Key Principles of DDSMore reliable/efficient systems using 1000’s of DER near loads• Increase efficiencies and reduced emissions through use of waste heat• Reduced transmission losses• More resilient system using local generation, microgrids& network reconfiguration

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Economic efficiencies via distribution-based marketplace• Utility Linked and Independent Distribution System Operators• Distributed and Local balancing authority • Distributed and Local marketplace

Simplify the central generation planning and operation• Handle distribution system’s dynamics locally (minimize volatility at the T-D interface)• Improve efficiencies by increasing base load operation. • Constant/contracted wholesale energy transactions.• Minimize CO2content

DDS Challenges

• Control Architecture Effectiveness Across all Distribution Configurations.

• Control and Interoperability standards that allow true “plug and play”

• Evolution of Grid Economic Models, Policies, Regulations

• Gaining acceptance of a new architectural approach for the grid ....

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DDS Benefits• Highly Scalable and Upgradable Architecture• Intrinsic High Efficiency and Reliability (CHP , Autonomous Modes)

• Stable and Controllable – Handles Reserves, Voltage, Current Support Locally ( Close to

the Source of the disturbance)– Enables High Penetration of Renewables

• Supports shorter life cycle economics: Promises a better cost to performance model

• DDS improves the Bulk Grid ; it does not replace it...• DDS can support a better evolution for the grid

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A Path Forward• The DDS Team Recommends

– Accelerate the growing research in the Electrical Distribution Grid ...as a System... as an Architecture

– Gather the excellent resources working in different parts of the vineyard and consider a new model of Grid Architectural Development ....

– Take on the tough challenges of Economic Models, Regulations, Policies, and Standards as part of the R&D

– Find that new grid value proposition that pays off the cost of transition .....

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Contributions and Reference

• Many thanks to these contributors:Professor Thomas Jahns : University of Wisconsin- MadisonProfessor Emeritus Bob Lasseter: University of Wisconsin-MadisonDr . Victor Zavala : University of Wisconsin-MadisonProfessor Adel Nasiri: University of Wisconsin-Milwaukee

• References:– [1}Nov. 2014 PSERC ( Power System Engineering Research Center) Webinar– [2] R. H. Lasseter, “Smart Distribution: Coupled Microgrids,” Proceedings of the IEEE, vol. 99, no. 6, pp. 1074–1082, 2011.– [3 ] Alegria, Lasseter, et al., “CERTS µGrid Demo w/ Large-Scale Energy Storage and Renewable Gen.”, IEEE Trans. on Smart Grids, Mar. 2014.– [4] Magni, Lalo, and Riccardo Scattolini. "Robustness and robust design of MPC for nonlinear discrete-time systems." Assessment and future

directions of nonlinear model predictive control. Springer Berlin Heidelberg, 2007. 239-254.– [5] Zavala, Victor M., and Lorenz T. Biegler. "The advanced-step NMPC controller: Optimality, stability and robustness." Automatica 45.1

(2009): 86-93.– [6] Zavala, Victor M., and Lorenz T. Biegler. "Nonlinear programming strategies for state estimation and model predictive control." Nonlinear

model predictive control. Springer Berlin Heidelberg, 2009. 419-432.– [7] Haseltine, Eric L., and James B. Rawlings. "Critical evaluation of extended Kalman filtering and moving-horizon estimation." Industrial &

engineering chemistry research 44.8 (2005): 2451-2460.– [8] B. T. Stewart, “Plantwide Cooperative Distributed Model Predictive Control,” Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison,

Madison, WI, 2010.– [9] A.Venkat, “Distributed Model Predictive Control : Theory and Applications”, Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison,

Madison, WI, 2006.

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