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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
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
• 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
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
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