Computational Modeling for Resilient Electricity-Gas Infrastructures (CMREX)

Milos Cvetkovic, Neha Nandakumar, Dylan Shiltz, Anuradha Annaswamy

TAI Conference, October 30, 2015, Washington DC

2

Interdependence between Natural Gas & Electricity Infrastructures

1. Newsham, Jack. “Electric rates in Mass. Set to spike this winter.” Boston Globe 25 Sept. 2014:. Print.2. http://americaspower.org/sites/default/files/Electricity-price-spikes_Feb_2014.pdf

Much needed are operations and market solutions that increase coordination of interconnected natural gas and electricity grids

ISO-NE fuel mix on October 29, 2015, 7:10 PM

High penetration of natural gas

[2]

[1]

September 25, 2014

Implications of high dependancy

Cold weather

Gas-fired units are increasing due to:o reduced footprint and increased efficiency compared to coal,o higher penetration of volatile renewable power, ando the need to reduce energy imports from foreign sources.

CMREX goals and team

Challenges: Implications of• Gas network constraints on EI• Gas market structure on EI• Growing dynamic intermittency and

uncertainty due to high penetration of renewables

• New resiliency issues due to o ING-EI interactionso cyber-physical interactions

Team• Anuradha Annaswamy (MIT)• Ignacio Perez-Arriaga (MIT)• Christopher Knittel (MIT)• Alefiya Hussain (USC)

Goal• Assess the interdependency of ING-EI and propose actions for improving resilience

4

In this talk

• Implications of NG market structure on EI–Natural gas market modeling to address unequal access to gas issue

• Dynamic intermittency and uncertainty due to high penetration of renewables

–Dynamic market mechanism for higher adaptability of the electricity grid operations

• Gas is the generally greatest fuel source for electricity• Uncertainty in obtaining this fuel instigated Winter Reliability Program in past

couple winters• More uncertainty could exist in gas bids with increased intermittent renewable

penetration into electric grid

Uncertainty in Obtaining Natural Gas

Uncertainty in Obtaining Natural Gas

12:00 am

6:00 am

12:00 pm

6:00 pm

12:00 am

Intraday Nomination 1

Timely Nomination

Intraday Nomination 2

Intraday Nomination 3

6:00 am

12:00 pm

6:00 pm

12:00 am

EveningNomination

Electric Day

Gas Day New

• Misalignments in real-time market can introduce uncertainties as NGPPs are on non-firm contracts

• Less uncertainty in DAM due to FERC 809 ruling

Bids to ISO

Schedule Posted

Schedule Reposted

Re-bid for gas

Gas Dispatch Model examines different levels of access to spot market

Service

Natural Gas Infrastructure

Pipeline Operators

Gas Producers Main

Local Distribution Companies (LDC)

RCI Consumers

Interstate Pipelines

Marketers/Third Party Shippers

Local Distribution Companies (LDC)

RCI Consumers

Natural Gas Power Plant/Generation Companies

Distribution PipelinesCommodity Contracts

Capacity Contracts

Allocations to GenCos

Bids based on ISO dispatch

Electricity ISO

Development of computational model at intersection between GenCo and NG marketers

8

Marketer 1

Proposed Computational Gas Dispatch Model

Valuation function of customer provided to each sellerQuantity

of gas Price for gas Preferenc

e type

WTP for secondary release capacity

Price setter for firm capacity

Price setter for different contract types

Marketer 2

Marketer 3

Pipeline Company

Producer

Consumer 1

Consumer 2

Consumers/LDCs can be on interruptible or firm contracts for commodity and capacity

Marketer-consumer bilateral transactions (with or without bidding)

Transactions regarding sale of capacityTransactions regarding sale of commodity

Gas Dispatch Model

M1

M2

M3

C1

C2

• Posed as a constrained optimization problem•Optimization objective: Marketers’ profit• Constraints: Capacity, Feasibility • Mass balance•Demand: From NGPPs

Marketer-NGPP interaction

NG Dispatch model to each NGPP

Electricity Market

Spot price, Allocated quantity

Electricity Dispatch

Overall Structure of Analysis

Massachusetts Case Study

Gas Node 1

M1 M2 M3 M4 M5

NGPP1 NGPP2 NGPP3 NGPP4 NGPP5

Gas Node2

Gas Node4

Gas Node5

Gas Node 4

Gas Node 5

• From actual monthly gas demand, daily gas demand (LDC and NGPPs) calculated for state of MA from Sept 2014 – April 2015 •Actual data from five main receipt point nodes from the Algonquin pipeline•Assume 5 marketers of natural gas and 5 NGPPs owned by one generating firm • The generating firm has equal or unequal access to gas with the marketers (as LDCs)• Each marketer is tied to one node, and each of the 5 NGPPs are tied to one node

where each marketer can sell gas to any of the 5 NGPPs

Computational ModelingGas topology (MA data) and gas dispatch model

Matpower Case118 electricity market model

Gas Marketer

Natural Gas Power Plant

(NGPP),

Day Ahead Market Real Time Operations

Bilateral transaction between marketer and NGPP

NGPP bids to ISO

ISO-NE Electric

Dispatch

NGPP gets dispatched amount to produce

Renewable Penetration

Equal vs. Unequal Access to Secondary Release Capacity Market (SRCM)

More curtailment on interruptable contracts

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Implications of Renewable Intermittency given Unequal Access to SRCM

More curtailment during real-time intraday market if renewables do not produce

Remaining Challenges

• Accurate data for NGPP curtailments – how often do NGPPs get curtailed the gas they bid for and can’t produce what the ISO schedules them to produce?– Complete or partial curtailment?

• How often do NGPPs submit re-offers to the ISO in the real-time market? Does this happen often after knowledge of gas procurement?– What are the implications for uncertainty in gas procurement?

In this talk

• Implications of NG market structure on EI–Natural gas market modeling to address unequal access to gas issue

• Dynamic intermittency and uncertainty due to high penetration of renewables

–Dynamic market mechanism for higher adaptability of the electricity grid operations

The need for higher adaptability of real-time operations

1. Volatile, uncertain and intermittent renewables2. Variable amount of responsive demand3. Fuel-uncertain natural gas units

Real-time operations must be highly adaptable to the change in operating conditions

Our solution: Dynamic Market Mechanism (DMM)

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Current practice – economic dispatch

Economic dispatch is:1) Periodic with a regular interval, 2) Single iteration process, 3) Computed centrally by the ISO.

Economic dispatch interval Time

Inflexible load

Generationset-points

Automaticgenerationcontrol

Collect cost curves Find optimal dispatch Communicate set-points

ISO

Flexibledemand

Generation

Automatic generation control is:1) Faster process than economic dispatch, 2) Centralized control computed by ISO.

Our Solution: Dynamic Market Mechanism (DMM)

Time

Generationset-points

Economic dispatch interval

Automaticgenerationcontrol

Inflexible load

Start negotiations

Negotiate and converge to an optimal solution

Implement set-pointsSufficiently long period

for convergence

DMM characteristics:1) Most recent information is included. 2) Individual constraints remain private.

Benefits when addressing:o Fuel uncertainty

• Wind• Solar• Natural gas

o Change in operating conditionsof components

• Saturation limits• Protection tripping• Emergency conditions

o Dynamic price response• Close-loop price control

Iterative negotiations with DMM

Marginal Cost/Bid [$/MW]

Suggested price [$]Suggested dispatch [MW]

GenCosminimize

cost

ConCosmaximize utility

ISOcompute optimal price

Aggregated error

Electrical frequency

Power grid

Consumed power

Generatedpower

AGC update

Cleared price [$]Cleared dispatch [MW]

DMM negotiations30 ms

DMM dispatch30 s

AGC updates4 s

DMM communication time-scales

Adaptability:DMM includes information on the most recent grid conditions at 30 ms intervals

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DMM Iterates Final Form*

State and price update equations

Approximated Hessian• Increases rate of convergence• Preserves privacy

Modified power balance• Integrates real-time market and AGC

Distributed gradient updates• A single cost/utility bid per iteration• Preserves privacy

[*] D. Shiltz, M. Cvetkovic, and A.M. Annaswamy, “An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation”, submitted to IEEE Transactions on Sustainable Energy, 2015.

Stability• Convergence is guaranteed when a

unique equilibrium exists, with a small step size , and the use of barrier functions to accommodate constraints

Conventional generation

Renewable generation

Demand response

Voltage angles

Modified IEEE 118 Bus Test Case

Wind Generator (30%)

Demand Response (10%)

DMM Market Clearings (50 clearings)

0 500 1000 15001400

1600

1800

2000

2200

2400

2600

Time [s]

Gen

erat

ion

[MW

]

0 500 1000 1500

245250255260265270275

Flex

ible

Dem

and

[MW

]

P G cP G rP D r

30 s

Negotiations over a single 30 second period

1110 1115 1120 1125 1130 1135 114022

24

26

28

30

32

34

36

Time [s]

Flex

ible

Con

sum

ptio

n [M

W]

1110 1115 1120 1125 1130 1135 11400

50

100

150

200

250

300

350

400

Time [s]Co

nven

tiona

l Gen

erat

ion

[MW

]

Conventional generation

Renewable generation

Demand response

Voltage angles

Actual Generation and Demand (AGC time-scale)

0 500 1000 15001400

1600

1800

2000

2200

2400

2600

Time [s]

Gen

erat

ion

[MW

]

0 500 1000 1500

245250255260265270275

Flex

ible

Dem

and

[MW

]

P G cP G rP D r

Impact on Area Control Error

• Peaks less severe using DMM than OPF• Adding feedback shifts ACE closer to zero

Number of iterations to Convergence

Matpower test cases

Demonstrates the scalability of the DMM

The convergence time depends on:• Step size • Congestion• Cost curves

Number of iterations does not increase with decision variables

DMM - Dispatch vs. Control Implementation

Set-point

Secondary control

Aggregated error

Set-point

Secondary control

Dispatch implementation [1] Control implementation [2]

Limitations• Requires new communication channels• No DR energy payback (no look ahead)• No optimal equilibrium for AGC

Benefits• Uses AGC dedicated channels• DR energy payback guaranteed• Economically efficient AGC

[1] D. Shiltz, M. Cvetkovic, A.M. Annaswamy, “An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation”, submitted to IEEE Transactions on Sustainable Energy, 2015, http://dspace.mit.edu/handle/1721.1/96683.[2] D. Shiltz, A. Annaswamy, “A Practical Integration of Automatic Generation Control and Demand Response”, IEEE American Control Conference, Boston, MA, July 2016, submitted, http://dspace.mit.edu/handle/1721.1/99356

Characteristics• Negotiations period 30 ms• Max convergence time 30 s

Characteristics• Negotiations period 2 s• Iterates used as control input during

negotiations

Remaining Challenges

• Integrate Natural Gas markets into DMM– Allows simultaneous optimization of both networks

• Model the dynamics of NG networks in detail– Actions taken on the electricity side (by NG generators for example)

may have effects on the gas network, affecting other generators and users

– Ultimate goal: detailed dynamic analysis of coupled networks using real-time decision making

Recent publications http://aaclab.mit.edu/smart-grid.php

• D. Shiltz, M. Cvetkovic, and A.M. Annaswamy, “An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation,” http://dspace.mit.edu/handle/1721.1/96683 (submitted to IEEE Transactions on Sustainable Energy).

• D. Shiltz, A. Annaswamy, “A Practical Integration of Automatic Generation Control and Demand Response”, IEEE American Control Conference, Boston, MA, July 2016, submitted, http://dspace.mit.edu/handle/1721.1/99356

• M. Cvetkovic, A. M. Annaswamy, “Coupled ISO-NE Real-time Energy and Regulation Markets for Reliability With Natural Gas”, IEEE Power and Energy Society General Meeting, July 2015.

• N. Nandakumar, A. M. Annaswamy, M. Cvetkovic, “Natural Gas-Electricity Market Design Utilizing Contract Theory”, IEEE Power and Energy Society General Meeting, Poster session, July 2015.

• A. M. Annaswamy, A. Hussain, A. Chakrabortty, M. Cvetkovic, “Foundations of Infrastructure CPS”, IEEE American Control Conference, Boston, MA, July 2016, submitted, http://dspace.mit.edu/handle/1721.1/99195

• M. Cvetkovic, A. Annaswamy, “Frequency Control using Cooperative Demand Response through Accumulated Energy”, IEEE American Control Conference, Boston, MA, July 2016, submitted, http://dspace.mit.edu/handle/1721.1/99194

• J. Hansen, J. Knudsen and A. M. Annaswamy. "A Dynamic Market Mechanism for Integration of Renewables and Demand Response,” IEEE Transactions on Control Systems Technology, to appear, DOI 10.1109/TCST.2015.2476785

• S. Jenkins and A.M. Annaswamy, “A Dynamic Model of the Combined Electricity and Natural Gas Markets,” IEEE PES Innovative Smart Grid Technologies Conference, February 2015.

• A. Kiani and A.M. Annaswamy. “A Dynamic Mechanism for Wholesale Energy Market: Stability and Robustness”, IEEE Transactions on Smart Grid, 5(6):2877-2888, November 2014.

• A. Kiani, A.M. Annaswamy, and T. Samad, “A Hierarchical Transactive Control Architecture for Renewables Integration in Smart Grids: Analytical modeling and stability,” IEEE Transactions on Smart Grid, Special Issue on Control Theory and Technology, 5(4):2054–2065, July 2014.

• A. Kiani and A. M. Annaswamy. “Equilibrium in Wholesale Energy Markets: Perturbation Analysis in the Presence of Renewables”, IEEE Transactions on Smart Grid, 5(1):177–187, January 2014.

mcvetkov@mit.edu nehan@mit.edu

supported by NSF grant no. EFRI 1441301

A snapshot of our multi-timescale approach

1. Allows flexible consumers to act as price-setters at the real-time market

2. Admits the most recent weather predictions in market clearing (ex. every 30 seconds)

3. Enables feedback from AGC layer into the market layer, reducing regulation requirements

4. Preserves privacy of market players’ sensitive information– e.g. cost curves, generation/consumption bounds

Is this scalable?

Polish 3120 Bus Test System

Data source: MatpowerFigure source: Paul Hines, “Estimating and Mitigating Cascading Failure Risk”, JST-NSF-DFG-RCN Workshop, April 2015

The system consists of:•3120 buses •3693 transmission lines with line capacities of 250 MW•505 generators with linear cost curves and capacities in the range 10MW-150MW•Extension to renewable energy resources and demand response is straight forward.

Single DMM Clearing

30ms per iteration

=30 s

Transmission line flows Power generation

Locational marginal prices

Generation and price increase at bus 3010 once three transmission lines reach their limits.

Line 59congestion

Lines 31,32congestion

Integrated DMM (economic dispatch + AGC)

Energy Market

Regulation Market

AutomaticGenerationControl

Assumption of magnitude and time-scale separation between OPF and AGC.

Large penetration of intermittent energy represents a challenge.

Conventional architecture

Energy Market

Regulation Market

AutomaticGenerationControl

Aggregated feedback from AGC

Simultaneous decisions at both markets.

Proposed approach

http://www.eia.gov/naturalgas/weekly/archive/2015/03_05/index.cfm

• Supply of natural gas growing • Demand/consumption for natural gas growing in electric sector• More curtailments of natural gas• Is there an optimal contract design that has all players satisfied (maximized utility of

attaining gas)?

Natural Gas-Electric Interdependence Increasing