A Smarter Dispatch for Smart Grid - University of Washington...2011/11/17 · −Robust Commitment...

A Smarter Dispatch for Smart Grid

Dr. Xing Wang

Nov 17, 2011 @ UW

Outline

• Smart Grid’s Impacts on Grid and Market Operations

• Smart Dispatch

• PJM Implementation Example

• Dispatch Demand Response

• Future Development

Smart Grid’s Impacts On Grid And Market Operations

Smart Grid’s Impacts on Grid and Market Operations

Smart Dispatch

Smart Dispatch Market Drivers

• Society’s growing dependency on electricity − Looming risks of major system disturbances − Focus on reliable demand-supply balance & grid security

• Global energy & environmental movement

− Increasing presence of DER - Distributed Energy Resources (e.g. wind, DR, etc) − Operational challenges: DER performance uncertainties

• Experiences from deregulated system operations

− Established foundation for integrating demand-supply balance with grid security constraints (e.g. LMP). − Identified emerging deficiencies in Dispatch Instructions

• Unrelenting complexity in business & technical decision process

Smart devices/resources with distributed intelligence

Coordinated decision making

Resource Scheduling and Dispatch Evolution of Capabilities

• Classical Dispatch

− Unit Commitment Scheduling, Economic Dispatch, AGC − Grid security, scheduling, dispatch are Independent tasks

• Market-Based Dispatch

− UC/ED with explicit transmission security constraints − Formal Day-Ahead and Real-time tasks − Pricing - Dual of the MW signal − Transparency & consistency − Large-scale system dispatch

• Smart Dispatch

− Dispatch with explicit forward vision − Dispatch with intelligence (e.g. parameter adaptation) − Improve system robustness against uncertainties (e.g. DER, Wind, DR) − Mitigate root-causes for dispatch deficiencies − Process re-engineering for business/economic analysis

Challenges in Demand Forecast and Reserve Estimation

• Increase amount of uncertainties

• Sources (Demand Response, distributed energy resources, wind)

• Demand forecast

• Need better predictability

Dimensions of Smart Dispatch Applications

Time

Scen

ari

os

AFA

SKED 3

SK

ED

-DA

SK

ED

1

SK

ED

2

PD

(Ro

bu

stn

es

s)

(Forensic

Analysis) (Vision)

Comprehensive Operating

Plan (COP)

SKED 1

t=30,60,90,120,180...720..

SKED 2

t=15,30,45,60...

SKED 3

t=5,10,15...5 min

Quarter

Hourly

Hourly

Outages

Demand Forecast

Adaptive Model

Management

5 min to

Hourly

On Demand

Asynchronous

Explicit Real-time

Dispatch

Implicit/Indirect DispatchAfter-the-Fact

Forensic Analysis

Perfect Dispatch

5 minOn Demand

5 min On

Demand

Physical System

OperationArchived System

Operation History

5 min

5 min On Demand

On

Demand

Smart Dispatch Overview

Adaptive Generator Model

• Operational history of resource used to predict response to certain dispatch instructions

• Predicted response used in determining dispatch instruction to be issued to resource

• Probabilistic response model replaces need to correct bad offer data or explicitly model mill points, dead bands, forbidden zones etc.

Adaptive Constraint Model

• Provide analysis to evaluate near-term line loading trends (and PV characteristics) to adapt constraint control strategy based on current conditions

• Group, rank and prioritize active transmission constraints

• Incorporate historical data to assist in anticipating and strategizing constraint control actions

• Reduce number of “hard” limits that are introduced into the optimization phase

SKED Optimization Engine

• SKED is a Mixed Integer Programming (MIP) / Linear Programming (LP) based optimization application which includes both unit commitment and unit dispatch functions. SKED can be easily configured to perform scheduling processes with different heart beats and different look-ahead time.

− Robust Reliability Unit Commitment (SKED1)

• Look ahead 6-12 hours with hourly intervals − Short-Term Look-Ahead Scheduling (SKED2)

• Look ahead 2-4 hours with 15-minute intervals − Real-time Dispatch and Pricing (SKED3)

• Look ahead 1 hour with 5-minute intervals

• Physical unit commit against the forecasted system conditions with consideration of uncertainties.

− Robust Commitment for slow start units

• Load and Generation Variations

• Interchange Variation

• Topology Uncertainty − Identification of Pre-ramping needs

• A multi-period security constrained unit commitment.

− Stochastic Approach − Robust Approach − Scenario-based Approach

Robust Reliability Unit Commitment

• Commitment Decision:

− Robust commitment of fast-start units only

• Scheduling Recommendation:

− Interchange schedules − Selection of regulation units − Demand response activation − Outage and ramping preposition

• Condition Prediction:

− Emergency conditions: capacity excess and shortage − Price prediction − Transmission congestion prediction

Short-Term Look-Ahead Scheduling

• Clears the real-time energy and ancillary service markets.

• Solves a multi-period security constrained economic dispatch problem:

− Balance the load while maintaining the system security. − Schedule the energy and ancillary service outputs for dispatchable

resources. − Compute the market clearing prices (ex ante or ex post) − Consider more accurate unit characteristics (start-up and shut down

profile, forbidden zone etc…)

Real-time Dispatch and Pricing

Comprehensive Operating Plan (COP)

• COP is an application to coordinate scheduling data to and from a certain class of power system applications and present a comprehensive, synchronized and more harmonized view of scheduling data to applications, to system operators or other stakeholders for the purpose of power system operations.

COP3

SCED3

Synch

COPt

COP2

SCED2

Synch

COP1

SCED1

Synch

AGM

Other data

ACM

AGM

Other data

ACM

AGM

Other data

ACM

Overrides MOI

SKED3

SKED2

SKED1

t0 t1 t2 t3 tn t1(sc2)

SKED-2 (M)

Low

SE time

stamp

Pgmin(tn)

....

SE

Pgmax(tn)

High

Medium

t2(sc2) t3(sc2)

Coordination Between Schedulers for Reach-ability

Envelope of reachability

After-the-Fact Analysis

• After-the-Fact Analysis (AFA)

Systematic analysis of past events and practices

Quantitative assessments of how do specific events and practices affect system performance

Use-case comparative approach

• Perfect Dispatch

A class of use cases of AFA that focus on “Day-After” analysis of the performance of actual system dispatch results, including the impacts of specific events.

Smart Dispatch Visualization

PJM GCA Implementation Example

From Andy Ott’s presentation at Panel “Smart Dispatch For Grid and Market Operations”, IEEE PES GM 2011.

»PJM©2011

»Day Ahead and Real Time Market Cycle

1. System Outage Analysis and Coordination

2. Day Ahead Market Clearing

3. Reliability Assessment Commitment

4. Real Time Market CT

Commitment

5. Real Time Market Economic

Dispatch

6. Perfect Dispatch Analysis

»PJM©2011 »24 »www.pjm.com

Observations

• Adaptive Models enhance performance of dispatch engines

• Real-time Optimization performance must improve to support automation

• Trend visualization well received by dispatchers

• Market response will be enhanced by reduction in dispatch base point volatility and producing dispatch signal trajectories for all resources

A Multi-tier Framework For Demand Response Dispatch

Price Responsive Demand

• Enabled by “Smart Grid”/AMI technology

• Residential and small commercial consumers

• Not directly dispatchable by RTOs/ISOs

• Aggregated to Transmission bus level

• Price Responsive/retail dynamic pricing

• Includes Distributed Energy Resources (DERs)

• Have significant impact to market and grid operation

Multi-Tier Optimization Framework

• Each tier has its own objectives and constraints

• Information flow between tiers is bi-directional. Coordination is required between tiers but it should be asynchronous.

• Parent tier provides a “bulk” guideline/incentive over a number of look-ahead intervals for its child tiers

• Child tier provides aggregated feedback (forecast MW values) over a number of look-ahead intervals to its parent tier


• RTO/ISO Tier − Support regional transmission level objectives − Transmission network model and bulk generation/load model − Distributed resources and demand responses are modeled as aggregation

(either MW forecast or MW-Price curves) − Price/incentive signals are at aggregated/bus pnodes level − Annual/monthly/weekly/day-ahead/intra-day/real-time coordinated

rolling scheduling/dispatch processes

• Market participant/utility tier − Portfolio optimization − Process the LMP received from RTO − Aggregation function − DMS function provider


• Aggregators/Commercial building Owners

− Detailed neighborhood/building modeling − Distributed resource/storage devices scheduling based on

upstream incentive signals − Local network optimization/feeder reconfiguration for loss

reduction, etc. − Provide aggregated MW feedback signals to upstream tier − Request distribution network information from utility DMS

• Household scheduling/distributed resource optimal control

− Monthly electricity bill reduction − EV/PHEV charging scheduling − Smart appliance optimal control

Future Development

• Advanced algorithms to deal with uncertainties

− Stochastic unit commitment − Robust unit commitment

• Computational performance enhancement

− Problem decomposition and parallel solving − Utilizing multi-core technology

• Distributed optimization

• Cloud computation

Thank You!

A Smarter Dispatch for Smart Grid - University of Washington...2011/11/17 · −Robust Commitment...

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