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Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 1 Expansion Planning for the Smart Grid Russell Bent Los Alamos.

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Presentation on theme: "Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 1 Expansion Planning for the Smart Grid Russell Bent Los Alamos."— Presentation transcript:

1 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 1 Expansion Planning for the Smart Grid Russell Bent Los Alamos National Laboratory LA-UR Joint work with G. Loren Toole, Alan Berscheid, and W. Brent Daniel SAMSI Scientific Problems for the Smart Grid Workshop 2011

2 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Slide 2 Outline

3 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 3 grid control grid stability LANL Project: Optimization & Control Theory for Smart Grids Network optimization 30% 2030 line switching distance to failure cascades demand response queuing of PHEV reactive control voltage collapse grid planning

4 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA M. Chertkov E. Ben-Naim J. Johnson K. Turitsyn L. Zdeborova R. Gupta R. Bent F. Pan L. Toole M. Hinrichs D. Izraelevitz S. Backhaus M. Anghel N. Santhi T-division D-division MPA CCS optimization & control theory statistics statistical physics information theory graph theory & algorithms network analysis operation research rare events analysis power engineering energy hardware energy planning & policy N. Sinitsyn P. Sulc S. Kudekar R. Pfitzner A. Giani 12 summer students >30 visitors (via smart grid CNLS/DR seminar) plus

5 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 5 grid control grid stability LANL Project: Optimization & Control Theory for Smart Grids Network optimization 30% 2030 line switching distance to failure cascades demand response queuing of PHEV reactive control voltage collapse grid planning Focus of this talk: How should smart grids be designed or planned?

6 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Slide 6 Outline

7 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Internal Nodes (buses) Power Consumers (loads) Power Generators Traditional Expansion Planning

8 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Internal Nodes (buses) Power Consumers (loads) Power Generators + Upgrade (transmission lines, shunt compensation, renewable generators) an electric power system to accommodate changes in demand and meet renewable generation goals Eliminate constraint violations (line overloads and voltage violations) Minimize expansion cost Reliability constraints + Traditional Expansion Planning

9 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Reduce the need to expand Demand response modeled as generators at load points Antunes et al 2004 (and others) Transmission switching Khodaei et al 2010 Peak reduction analysis (Demand Response) Olympic Pennisula Project (PNNL) Increase the need to expand Large penetration of renewables Backup generation Storage Transmission capacity Placement of monitors and controls Microgrids/Distributed Generation Electric Vehicles Slide 9 Smart Grid Impacts to Planning Operations can impact how systems are expanded.

10 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Extendable to incorporate other types of expansion options Challenges Expansion may introduce physical violations (Braesss paradox) Highly non-linear, generally considered intractable Slide 10 P i = k=1..n |V i ||V k |(c ik g ik cos(Θ i -Θ k ) + c ik b ik sin(Θ i -Θ k )) Q i = k=1..n |V i ||V k |(c ik g ik sin(Θ i -Θ k ) + c ik b ik cos(Θ i -Θ k )) P i = Real power of bus i Q i = Reactive power of bus i V i = Voltage of bus i Θ i = phase angle of bus i g ik = conductance between i,k b ik = susceptance between i,k c ik = number of circuits between i,k Expansion Planning Optimization Model

11 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Linearized DC approximation Slide 11 P i = k=1..n b ik (Θ i -Θ k ) Still a mixed integer non-linear program (can be converted to an integer program) P i = k=1..n b ik c ik (Θ i -Θ k ) Modeling assumptions Minor changes in V and Θ AC (Q) power a small contributor Controllable generation Considered straight-forward by planners to modify a TNEP solution to more complex flow representations Not clear if these assumptions continue to hold when planning for smart grid and renewables Reduced Expansion Planning Optimization Model Revisit the more complex models to better plan for smart grid, operations, renewables, etc.

12 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Slide 12 Outline

13 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 13 Optimization Simulation Expansion Decisions Power flow behavior Encapsulate models difficult to represent in a black box (simulation) Typically used to evaluate objective function or feasibility Simulation results inform optimization choices Algorithm decoupled from the details of how power flows are modeled Algorithm Intuition: Simulation Optimization

14 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Existing Approaches Experimental Results Slide 14 Outline

15 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Advantages Complete (Optimal Search) Disadvantage Computationally burdensome Example: Add wind generator to bus 1 Do not add wind generator to bus 1 Branch and Bound Simulation…

16 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Advantages Complete (Optimal Search) Disadvantage Computationally burdensome Example: Add wind generator to bus 1 Do not add wind generator to bus 1 Branch and Bound

17 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Advantages Computationally efficient Disadvantage Local optimality Add wind generator to bus 1 Add 1 circuit to corridor 3 Add wind generator to bus 9 Constructive Heuristic

18 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Existing Approaches Our Approach (Hybridize) Experimental Results Slide 18 Outline

19 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Hybridize the two approaches Constructive heuristic is used as the branching heuristic Still computationally expensive … Discrepancy Bounded Local Search – DBLS (Approach 1)

20 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Solution: Explore solutions near the heuristic Up to δ distance (discrepancies) from the heuristic Similar to Limited Discrepancy Search (Harvey & Ginsberg 95) Artificial Intelligence Community Running time exponential in δ 1 Discrepancy Discrepancy Bounded Local Search – DBLS (Approach 1)

21 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Randomized Constructive Heuristic – RCH (Approach 2) For any node in the search tree, order the expansion options by the constructive heuristic Choose the i th option, where i = (RANDOM([0,1]) ß * # possible expansions) Shown useful on other combinatorial problems Repeat the search multiple times to find alternate solutions

22 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Enhancements Execute simulation (power flow) for each partial solution Prune when partial solutions degrade solution quality too much RCH and DBLS

23 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Existing Approaches Our Approach (Hybridize) Branching Heuristics Experimental Results Slide 23 Outline

24 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Choose the expansion that improves the partial solution the most Bustamante-Cedeno and Arora 09, Romero et al 05, etc. Requires a linear number of simulations at each node Constructive Heuristic: Most Improving (MI)

25 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Add lines where capacities are violated Line additions can increase flow in the area Constructive Heuristic: Max Utilization (MU)

26 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Consider the neighborhood of an over- capacity edge Add capacity to edges that remove power from the upstream neighborhood or add power downstream Constructive Heuristic: Flow Diversion (FD)

27 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Add lines on alternate paths that bring power to downstream nodes Constructive Heuristic: Alternate path (AP)

28 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide Add lines on alternate paths that bring power from a generator to a downstream load Constructive Heuristic: Alternate path around (APA)

29 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Transmission Expansion Slide 29 Outline

30 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Description Grew Loads and Generation of IEEE RTS-79 by % 24 buses, 41 transmission corridors, 8550 MW of load Expand with up to 3 additional circuits in each existing, and up to 3 circuits in 8 new corridors Highly constrained Referred to as G1, G2, G3, G4 Slide 30 IEEE Expansion Benchmarks (Feng and Hill, 2003)

31 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 31 Comparison of results for different heuristics

32 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 32 Comparison of results for different heuristics

33 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 33 Comparison of two algorithms

34 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 34 Comparison of two algorithms

35 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 35 Comparison with Existing Approaches ProblemBest KnownRefBest Found G1438KRRMS390K G2451KFH392K G3218KRRMS272K G4376KFH341K Solutions to the DC model RRMS = Romero et al 05, FH = Feng and Hill 03

36 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA AC modeling vs. DC modeling DCAC G1390K1316K G2392K1977K G3272K1003K G3341K1978K Expansion based on AC modeling considerable more expensive than DC modeling Empirical evidence of the importance of using complex power flow models Problem is very constrained (no dispatchable generation, DC solution maxes some expansions, high percentage of reactive power, limited shunt compensation expansion options) If these constraints are relaxed, the cost gap can be substantially reduced Feng and Hill benchmarks based on IEEE 24 Bus RTS problems

37 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Expand the New Mexico Grid 2020 load and generation projections for New Mexico 1700 MVA of overloads in 31 corridors 30 circuits added to 28 corridors 300 Million in expansion costs

38 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 38 Path Flow congestion 2030 AC Power Flow Model Expand for WECC

39 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Transmission Expansion Transmission and Generation Expansion Slide 39 Outline

40 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Existing benchmark Grew Loads and Generation of IEEE RTS-79 by % 24 buses, 41 transmission corridors, 8550 MW of load Expand with up to 3 additional circuits in each existing, and up to 3 circuits in 8 new corridors Referred to as G1, G2, G3, G4 Our additions Scale generation back to RTS-79 levels, make this a decision variable Generation expansion costs roughly inline with transmission costs See paper for the details Slide 40 IEEE Benchmarks (Feng and Hill, 2003)

41 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA DC model results

42 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA DC model results (G1) BusGeneratorsCost 1440K 2480K 74158K K 1400K 15436K 16315K K 2100K K K CircuitLinesCost 1,200K 1,500K 2,400K 2,600K 3,2400K 5,1000K 6,700K 6,10116K 7,8232K 8,1000K 10,12150K 10,1100K 11,13166K 14,1600K 15,2400K 16,1700K 1913K 164K

43 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA AC Model Results

44 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA AC model results (G1) BusGeneratorsCost 1440K 2480K 74158K K 1400K 15436K 16315K K 2100K K K CircuitLinesCost 1,213K 1,5122K 2,4133K 2,63150K 3,24150K 5,10369K 6,73150K 6,1000K 7,8348K 8,103129K 10,1200K 10,112100K 11,13166K 14,16154K 15,24172K 16,17136K 1854K 982K Constraints play a large role again

45 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA U.S. Department of Energy demand predictions for buses selected for renewable expansion (2 solar, 5 wind) from New Mexico renewable development study: 5, 10, and 20-year transmission collection, Technical Report LA-UR Solution builds bulk of new generation in Springer and Guadalupe areas 800 MVA in line overloads in 30 transmission corridors Solution adds 53 lines in 41 corridors New Mexico Case Study

46 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA New Mexico

47 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Brief Overview of Smart Grid Research at Los Alamos Grid Expansion Planning Model Grid Expansion Planning Algorithm Experimental Results Transmission Expansion Transmission and Generation Expansion Expansion with Grid Operations and Control Slide 47 Outline

48 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Consider how adding renewable generation does/does not reduce carbon emissions Based on Feng and Hill 03 TNEP RTS-79 problems (again) 7 versions requiring the addition of 100, 200, 300, 400, 500, 1000, 2000, 3000 MW must take renewable energy Can be added to buses 1, 2, 7, 13, 15, 16, 18, 21, 22, and 23 (existing generation sites) Model operations through the DC OPF Carbon emissions and operational costs taken from EIA Annual Energy Outlook AC OPF is future work Slide 48 Example 1: Reduction of Carbon Emissions

49 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Example 1: Reduction of Carbon Emissions RCH – includes grid operations LB – Lower bound on best possible carbon emissions UB – Upper bound on worst possible carbon emissions RCH Base – solution that does not include grid operations

50 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 50 Example 1: Reduction of Carbon Emissions

51 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 51 Example 1: Reduction of Carbon Emissions Multi- Scenario Expansion for 4 load scenarios

52 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Slide 52 Example 1: Reduction of Carbon Emissions Multi- Scenario Expansion for 4 load scenarios

53 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA New Mexicos transmission grid must be expanded to serve three purposes: [1] Meet projected load growth; [2] Increase utilization of renewables; [3] Maintain reliable delivery of power High Summer 2030 electric demand-supply based on WECCs planning assumptions 1 Four Corners transmission hub will continue to serve as New Mexicos primary means for exporting power 1 WECC: Western Electricity Coordinating Council; primary planning organization for the 14-state western United States Slide 53 Example 2: State-Level Collector and Export

54 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Collector Plan 1: Uprate 530 miles of existing corridors, construct 311 miles of new corridors Collector Plan 2: Uprate 849 miles existing corridors Slide 54 Collection Plan 1, 2 Grid Design (2030)

55 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA State-level Input/output IMPLAN model Estimates Direct, Indirect and Induced effects Demonstrates the need to address complex economic operations Slide 55 New Mexico renewable development study: 5, 10, and 20-year transmission collection, Technical Report LA-UR Economic Impacts: Collector Plan 1 versus Plan 2

56 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Demo

57 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA Highly constrained operations of a grid increase the need for complex (AC) power system modeling in expansion planning Operations and control of a grid can impact system expansions Future work PMU placement for cyber security vs. operational requirements Expansion for renewable intermittency (robust or probabilistic operational goals) Stronger robustness metrics Algorithm generalization to control problems (transmission switching) Slide 57 Conclusions and Future Work

58 Operated by Los Alamos National Security, LLC for the U.S. Department of Energys NNSA References R. Bent, G. Loren Toole, and A. Berscheid Transmission Expansion Planning with Complex Power Flow Models. IEEE Transaction on Power Systems (to appear) R. Bent, G. Loren Toole, and A. Berscheid Generation and Transmission Expansion Planning for Renewable Energy Integration. 17th Power Systems Computation Conference (PES 2011), August 2011, Stockholm, Sweden.PES 2011 R. Bent and W. Brent Daniel Randomized Discrepancy Bounded Local Search for Transmission Expansion Planning. Power Engineering Society General Meeting (PES 2011), July 2011, Detroit, Michigan.PES 2011 R. Bent, A. Berscheid, and G. Loren Toole. Transmission Network Expansion Planning with Simulation Optimization. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010), July 2010, Atlanta, GeorgiaAAAI 2010 L. Toole, M. Fair, A. Berscheid, and R. Bent. Electric Power Transmission Network Design for Wind Generation in the Western United States: Algorithms, Methodology, and Analysis. Proceedings of the 2010 IEEE Power Engineering Society Transmission and Distribution Conference and Exposition (IEEE TD 2010), 1-8, April 2010, New Orleans, Louisiana.IEEE TD 2010 R. Bent and G. Loren Toole. Grid Expansion Planning for Carbon Emissions Reduction. (under review) The information science developed here ported to the RETA study: New Mexico renewable development study: 5, 10, and 20-year transmission collection, Technical Report LA-UR


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