Small Resource Integration Challenges for Large Scale Day

© 2000-2016 New York Independent System Operator, Inc. All Rights Reserved. © 2000-2016 New York Independent System Operator, Inc. All Rights Reserved.

Small Resource Integration Challenges for Large Scale Day-Ahead Security

Constrained Unit Commitment Cuong Nguyen

Senior Market Technologies Research Engineer New York Independent System Operator

Control at Large Scales: Energy Markets and Responsive Grids University of Minnesota -- Institute for Mathematics and its Applications

Minneapolis, MN May 11, 2016

© 2000-2016 New York Independent System Operator, Inc. All Rights Reserved. 2

NYISO Facts & Figures New York State population 19 million

2015 Peak Load 31,138 MW

2015 Required Installed Capacity 39,273 MW

Record peak 33,956 MW (July 19, 2013)

Power Generation 700+ units

High-Voltage Transmission 11,000+ circuit-miles

Average Annual Market Transactions $7.5 Billion

Market Participants 400+


Unit Commitment is at the Heart of NYISO Market Systems

Input • Bids/Offers • Forecasts

Output • Least cost solution • Meet reliability needs

SCUC

Bids & Offers • GEN Supply • LSE • Virtual • Regulation • Reserve • Bilaterals • Import/Exports • Demand Response

Post Results • Clearing Prices

• LBMP (GEN & Zonal) • Reserve • Regulation

• GEN Schedule • Transaction Schedule • Bilateral Transactions • Reserve Commitment • Regulation Commitment

Security Constrained Economic

Commitment and Dispatch


Core Innovation & R&D Distribution System Platform Providers New York State Community PartnershipREV Campus Challenge

New York Green Bank: $1 Billion

Energy Storage R&D/Commercialization:NY-BEST & Brookhaven National Lab

Five Cities Energy Plans: 20% Reduction in Municipal Energy Consumption by

2020

Other Renewable Initiatives:K-Solar

Shared Renew ablesOffshore Wind Initiative

Community Choice AggregationAGILe:

$35 Million Lab - Smart Grid Technologies for Eff iciency, Reliability, Resilience

New York Sun Initiative:10-Year, $1 Billion

3,000 MW

Long Island "Utility 2.0" Plan:Smart Solar, Vehicle to Grid, Eff iciency

Smart Generation & Transmission:440MW Increased Pow er Flow on Existing Lines

Large-Scale Renewables:10-Year, $1.5 Billion

Bundled PPAs?

REV Business Model Demonstrations BuildSmart New York:20% Energy Use Reduction in State-Ow ned Buildings

Energy Efficiency Programs:NYSERDA, Utility-Based

New York Prize Community Microgrids:$40 Million

Energy Highway

Reforming the Energy Vision: Foundational Element of State Energy PlanGuiding Principles: Market Transformation; Community Engagement; Efficiency; Private Sector Investment; Innovation & Technology; Customer Value & Choice

2015 New York State Energy PlanComprehensive Roadmap to Build a Clean, Resilient, Affordable Energy System for All New Yorkers

Clean Energy Fund: $5 Billion 2016-2026Attract Private Capital

Greater Deployment/Maturity of Clean Energy TechnologySignif icant Greenhouse Gas Reductions

REV Proceeding:Facilitate Expansion of Distributed Energy Resource (DER) and Align

Utility BusinessModels to Support Clean Energy

NYPA Leadership:Inform pow er supply, and demand-side programs

Addressing Key Challenges for Goal Attainment:Clean, Resilient, Affordable, Regulatory Reform, Environmental Justice, Clean & Reliable Transportation

2030 Greenhouse Gas Goal:40% Emissions Reduction Relative to 1990 Levels

2030 Renewable Energy Goal:50% of Electricity Generation from Renew able Energy Resources

2030 Energy Efficiency Goal:23% Reduction in Building Energy Consumption

Majority of state funding to directly influence markets

http://energyplan.ny.gov/Plans/2015

http://energyplan.ny.gov/Plans/2015


REV Impact


Small Resources • Transmission Operator Distribution Operator

Contemplates many, smaller transactions Currently 1 MW minimum for NYISO

• SCUC optimization branch and bound technique leaves “MIP gap” Initial unit commitment may not be physically feasible, and SCUC must iterate to

achieve a least-cost unit commitment while respecting all system constraints Problem is bounded at a production cost limit The impact of small resources on the solution may be less than the production

cost limit. In this circumstance, a branch-and-bound Mixed Integer Programming (MIP) solution does not determine when such resources’ commitment will enhance efficiency

• Commitment of small resource will require: more processing power new optimization techniques, and/or an active role for distribution operators


Small Resource Integration

Notes: • Line impedances are equal • Units offer two equal MW segments between [Pmin, Pmax]

G1 500 1270 150 300 10 12G2 700 1660 205 305 10 11G3 200 310 105 135 12 21G4 200 316 105 135 12 19G5 300 430 55 95 13 23G6 3 8 1 3 10 11G7 2 3 1 2 12 20G8 6 8 1 2 13 20

Unit Startup Cost ($)

Mingen Cost ($/h)

Pmin (MW)

Pmax (MW)

Incremental Cost ($/MWh)


Small Resource Integration


Classic Branch-and-Bound MIP Solution Procedure

• Solution technique Branch-and-bound method is used to illustrate the solution procedure Depth-first search strategy is applied for the node selection operation Least fraction strategy is adopted to choose binary variables for the branching operation



Decomposition Approach for Solving

Small Resources


Tighter Reformulation Approach for Solving Small Resources*

Convex hull of an MIP problem is the smallest polyhedron that contains all integer feasible solutions. Convex hull formulation provides a tighter lower bound, and in turn could reduce the search effort of the branch-and-bound algorithm

Ideal: An MIP formulation is called ideal if vertices of the corresponding LP relaxation satisfy integrality requirements. That is the optimal solution to the original MIP problem can be directly obtained by solving the LP relaxation problem. However it’s hard to find the explicit convex hull formulation of the entire MIP problem

Local: convex hull formulation is sought for a specifically selected portion, which could help tighten the lower bound and in turn reduce the search effort of the BAB algorithm, especially when such a subset includes key binary decision variables of the SCUC problem.

* Lei Wu, “Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts,” IEEE Transactions on Power Systems, December 2015


Tighter Reformulation Approach for Solving Small Resources*

• General purpose branch-and-bound solvers like CPLEX, GUROBI may not be aware of the specific SCUC problem structure Unnecessarily branch on one of the many variables Resort to heuristic rounding approaches for obtaining integer feasible solutions

• Locally ideal reformulation Commitment variable Iit is closely related to dispatchable variable Pit Iit is largely dependent on the operation cost Cit Tighter reformulation can be achieved to dramatically reduce computational burden

* Lei Wu, “Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts,” IEEE Transactions on Power Systems, December 2015


Locally Ideal Reformulation for Branch-and-Bound MIP Solution Procedure

• Solution technique Branch-and-bound method is used to illustrate the solution procedure Depth-first search strategy is applied for the node selection operation Least fraction strategy is adopted to choose binary variables for the branching operation


Technical Challenges • System boundaries

How far we would like to push the boundary of the large scale day-ahead security constrained unit commitment?

• Solution techniques Decomposition approach? Tighter MIP reformulation to exploit special characteristic of energy system? Smarter cuts?

• Computing resources Hi-Performance Computing Cloud computing


The Mission of the New York Independent System Operator, in collaboration with its stakeholders, is to serve the public interest and provide benefit to consumers by:

• Maintaining and enhancing regional reliability

• Operating open, fair and competitive wholesale electricity markets

• Planning the power system for the future

• Providing factual information to policy makers, stakeholders and investors in the power system

www.nyiso.com

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Small Resource Integration Challenges for Large Scale Day