ps erc improving economic dispatch through transmission switching: new opportunities for a smart...
TRANSCRIPT
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Improving Economic Dispatch through
Transmission Switching:New Opportunities for a
Smart Grid“THE GRID AFTER NEXT”
Power Systems Modeling 2009
University of FloridaMarch 18-20, 2009
Kory Hedman and Shmuel Oren University of California, Berkeley
(Based on joint work with Richard O'Neill, Emily Fisher and Michael Ferris)
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Motivation Transmission planning addresses long term
problem and a broad range of contingencies so the grid is built with redundancies that may not be needed in every state of the system
Network redundancies motivated by reliability requirements may constrain generation dispatch create congestion and reduce economic efficiency
Transmission assets are currently seen as static in the short term and control of transmission assets for economic reasons is underutilized
Security constrained economic dispatch can be improved and congestion reduced through co-optimization of generation dispatch and the network active topology
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Motivation (cont’d) Optimal transmission switching does not
necessarily indicate inefficient transmission planning but such an option may affect planning
With appropriate Smart Grid switching technology, some backup transmission can be kept offline (just in time N-1)
Currently operators change transmission assets’ states on ad-hoc basis (per private communication with Andy Ott, VP, PJM)
Economically motivated transmission switching is consistent with FERC order 890 (supports transmission for economics)
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3-Bus Example
Line A-B:
Line B-C:
Line A-C:
50_3
1_
3
150 BGenAGen
80_3
2_
3
180 BGenAGen
200_3
1_
3
2200 BGenAGen
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5Transmission Switching 3 Bus Example
Original Optimal Cost: $20,000 Open Line A-B, Optimal Cost: $15,000
Original & New Feasible Sets
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Objectives and Scope Co-optimize transmission topology and generation
dispatch Efficiency improvements with no reliability
degradation Smart grid application by exploiting short term
reconfiguration flexibility Asses cost of achieving reliability through network
redundancy (e.g. N-1 criterion) Explore options and lay foundation for new reliability
concepts (just in time N-1) Explore market implications of dynamic transmission
switching and impact on transmission rights Proof of concept: IEEE 118, IEEE 73 (RTS 96), ISO-NE
5000 bus model
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Electric Transmission Network Problems
Generation Unit CommitmentOptimal Power Flow (OPF) Problems
– Alternating Current Optimal Power Flow (ACOPF)
– Direct Current OPF (DCOPF) – Linearization of the ACOPF
Reliability (Contingency) Requirements – N-1 Standards– N-1 DCOPF, Security Constrained ACOPF
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Relevant Literature “Corrective Switching”
– Changes the topology after the network optimization problem is complete to relieve constraints violations
– Feasibility, search problems
[Mazi, Wollenberg, Hesse 1986]: Corrective control of power systems flows (line overloads)
[Schnyder, Glavitsch 1990]: Security enhancement using an optimal switching power flow
[Glavitsch 1993]: Power system security enhanced by post-contingency switching and rescheduling
[Shao, Vittal 2006]: Corrective switching algorithm for relieving overloads and voltage violations
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Literature Review cont’d
“After the fact” switching to reduce losses– Does not incorporate transmission switching into the
overall OPF problem– Changes topology to reduce losses after dispatch
solution is known
[Bacher, Glavitsch 1988]: Loss reduction by network switching
[Fliscounakis, Zaoui, et al. 2007]: Topology influence on loss reduction as a mixed integer linear program
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Literature Review cont’d
Optimal Switching to Relieve Congestion– Similar MIP formulation– Aims at relieving congestion rather than co-optimizing
network topology and generation [Granelli, Montagna, et al. 2006]: Optimal network
reconfiguration for congestion management by deterministic and genetic algorithms
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Technical Approach
Traditional DCOPF Minimize: Total generation cost
Subject to: – Bus angle constraints– Generator min & max operating constraints– Node balance constraints– Line flow constraints– Line min & max operating (thermal) constraint
Initially we do not consider generation unit commitment (do not consider startup, no load cost or minimum load)
We represent Kirchhoff law in terms of the linear approximation
(Rather then the PTDF representation)( ) 0k n m nmkB P
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OPF Nomenclature
Variables:Pnmk (Qnmk): real (reactive) power flow through
transmission line k connecting buses m and n Png: Generator g supply at bus n
Vn: Voltage magnitude at bus n
θn: Bus voltage angle at bus n
zk: Transmission line status (1 closed, 0 open) Parameters:Bk: Susceptance of transmission line k
Pnd: Real power load at bus n
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Power Flow Overview AC Line Flow Equations (Kirchhoff’s laws):
Non-convex constraints Linearization of the ACOPF to get DCOPF DCOPF used in Academia & Industry DC Line Flow Equation:
(Alternative representation uses PTDFs)
0)( nmkmnk PB
)sin()cos( mnmnkmnmnknmk VVBVVGP
).cos()sin( mnmnkmnmnknmk VVBVVGQ
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Optimal Transmission Switching with DCOPF
Zk: Binary variable – State of transmission line (0 open, 1 closed)
Update line min/max thermal constraints:– Original:– New:
Update line flow constraints:– Original:– New:
maxminknmkk PPP
kknmkkk zPPzP maxmin
0)1()( kknmkmnk MzPB 0)1()( kknmkmnk MzPB
0)( nmkmnk PB
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Optimal Transmission Switching DCOPF
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Results - Summary IEEE 118 Bus Model:
– DCOPF transmission switching solution with no contingencies saves 25% of total generation cost (10 lines switched off)
– Up to 16% savings with N-1 DCOPF transmission switching (for feasible solutions)
IEEE 73 (RTS 96) Bus Model – Up to 8% savings with N-1 DCOPF
transmission switching (for feasible solutions)
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Results – DCOPF – IEEE 118
Transmission switching solution saves 25% of total generation cost
J
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Results – DCOPF – IEEE 118
IEEE 118 opened lines for J=10
Note: this diagram has additional gens than our model
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Results – DCOPF – IEEE 118
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Results – DCOPF – IEEE 118
Results are % of static network’s DCOPF solution
40%
60%
80%
100%
120%
140%
160%
180%
200%
J=0 J=1 J=2 J=3 J=4 J=5 J=6 J=7 J=8 J=9 J=10 Case1
Case2
Best
Generation CostGeneration RevenueGeneration RentCongestion RentLoad Payment
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Optimal Transmission Switching with
N-1 DCOPF
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N1 Binary Parameter
Incorporation of N-1 reliability constraints: N1ec: N-1 binary parameter specifying what element e
(transmission or generator) in the network is offline for state c
c=0 steady-state operations
c>0 contingency state
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23Optimal Transmission Switching
with N-1 DCOPF (cont’d)Transmission contingencies:
– Thermal ratings are set at emergency ratings– Generator dispatch is unchanged
Generation contingencies:– No ramp rate modeling of generators– Assume possible full redispatch of online
generators – Thermal ratings are set at emergency ratings
Determine modified N-1 contingency lists for test cases
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Contingency Assumptions
Transmission contingencies:– Thermal ratings are set at emergency ratings– Generator dispatch is unchanged
Generation contingencies:– Assume possible full re-dispatch of online
generators – Thermal ratings are set at emergency ratings
Determine modified N-1 contingency lists for test cases
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25Results – N-1 DCOPF IEEE 118
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26Results – N-1 DCOPF IEEE 118
Results are % of static network’s N-1 DCOPF solution
20%
40%
60%
80%
100%
120%
140%
J=0
J=1
J=2
J=3
J=4
J=5
J=6
J=7
J=8
J=9
J=10
IL1
(J=1
0)
IL2
(J=1
2)
J: No. of Open Tx Elements
$/h
% o
f J
= 0
Val
ue
Generation Cost Generation RevenueGeneration Rent Congestion RentLoad Payment
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Results – N-1 DCOPF – IEEE 73 (RTS 96)
Results are % of static network’s N-1 DCOPF solution
30%
40%
50%
60%
70%
80%
90%
100%
J=0 J=1 J=2 J=3 J=4 J=5J: No. of Open Tx Elements
$/h
% o
f J
= 0
Val
ue
Generation Cost Generation RevenueGeneration Rent Congestion RentLoad Payment
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Results - Summary
ISO-NE 5000 bus model– 5% to 13% savings
of $600,000 total cost for NEPOOL for one hour (feasible solutions)
Does not include reliability constraints
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Results – DCOPF – ISONE ISONE – Summer Peak Model (5000 bus network)
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Results – DCOPF – ISONE Results are % of static network’s DCOPF solution ISONE – Summer Peak Model
20%
40%
60%
80%
100%
120% Generation Cost Generation RevenueGeneration Rent Congestion RentLoad Payment
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Results – DCOPF – ISONE (cont’d)
Results are % of static network’s DCOPF solution ISONE – Connecticut Import Study Model
0%
20%
40%
60%
80%
100%
J=0 J=1 J=2 J=3 J=4 J=5 J=6 J=7 J=8 J=9 J=10 IL1
Generation Cost Generation RevenueGeneration Rent Congestion RentLoad Payment
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Multi-Period Model with Reliability Constraints
Generation Unit Commitment Multi-Period Model– Startup costs– Shutdown costs– Minimum up and down time constraints
• Facet defining valid inequalities
– No ramp rate constraints
Transmission SwitchingN-1 Contingency Constraints
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Generation Unit Commitment Nomenclature
Variables: ugt: Unit commitment variable (1 generator
online, 0 generator offline) vgt: Startup variable (1 generator turned on in
period t, 0 otherwise) wgt: Shutdown variable (1 generator turned off in
period t, 0 otherwise) Parameters: SUg: Startup cost, generator g SDg: Shutdown cost, generator g UTg: Minimum up time, generator g DTg: Minimum down time, generator g
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Multi-Period Formulation
Objective & Power Flow Constraints:
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Multi-Period Formulation cont’d Generation Unit Commitment Constraints:
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Results – 24HR Gen UC & Optimal Transmission Switching
N-1 DCOPF Model: IEEE RTS-96 system Results show:
– Optimal network topology varies from hour to hour– Changing the network topology can change the
optimal generation unit commitment solution – Total startup costs may be reduced– Peaker units initially required with original topology
were not required once transmission switching was incorporated into the problem
3.7% overall savings or over $120,000 (24hr) for this medium sized IEEE test case – can translate into millions for large scale networks for entire year
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37Results – Computational Statistics
IEEE 118 DCOPF & N-1 DCOPF variables & constraints:
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Computational Statistics cont’d
ISONE (DCOPF)
– To solve for best 2 lines to open, optimality not reached after 50 hours
– Used heuristic of finding next best line to open
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Revenue Adequacy in Financial Transmission Rights Market
FTRs: Hedging mechanism Market operator compensates FTR owners with
congestion rent (surplus) Revenue adequacy not guaranteed if topology
changes [Alsac, Bright, et al. 2004] Following example illustrate potential
congestion revenue shortfall due to tran.smission switching
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Revenue Adequacy of FTR s: Example
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Revenue Adequacy of FTRs Cont’d
Without Switching Line A-B In (Case 1):
With Switching Line A-B Out (Case 2):
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Total generation cost decreases but we can create FTR holdings that result in revenue inadequacy for the switching solution.We have revenue adequacy with the no switching solution (case 1) but we do not have it with the switching solution (case 2) even though it increases social welfare.
Revenue Adequacy of FTRs Cont’d
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Further Research Generation unit commitment for Day Ahead scheduling
with Transmission Switching– Speed up B&B by introducing valid inequalities that exploit the
problem structure– Explore use of interior point methods for relaxed problems at B&B
nodes (for upper bounds computation) – Explore heuristic techniques, separation techniques, etc.
Benders’ decomposition– Analyze various sub-problem formats– Research local branching approach
Use AC OPF for short term (e.g. hourly) switching problem– MINLP very difficult– Research heuristic techniques
Revenue adequacy & FTR settlement– Can revenue adequacy be maintained with transmission switching?– Develop a compensation scheme to offset the impact on FTR
settlements?
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44Documentation and Publications
[1] K. W. Hedman, R. P. O’Neill, E. B. Fisher, S. S. Oren, “Optimal transmission switching – sensitivity analysis and extensions,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1469-1479, Aug. 2008.
[2] K. W. Hedman, R. P. O’Neill, E. B. Fisher, S. S. Oren, “Optimal transmission switching with contingency analysis,” IEEE Trans. Power Syst., to be published.
[3] K. W. Hedman, R. P. O’Neill, S. S. Oren, “Analyzing valid inequalities of the generation unit commitment problem,” Proc. Power Syst. Conf. and Expo. (PSCE), March 2009.
[4]E. B. Fisher, K. W. Hedman, R. P. O’Neill, M. C. Ferris, S. S. Oren, “Optimal transmission switching in electric networks for improved economic operations,” INFRADAY Conference 2008.
[5] K. W. Hedman, M. C. Ferris, R. P. O’Neill, E. B. Fisher, S. S. Oren, “Multi-period generation unit commitment and transmission switching with N-1 reliability,” IEEE Trans. Power Syst., submitted for publication.
[6] K. W. Hedman, R. P. O’Neill, E. B. Fisher, S. S. Oren, “Flexible transmission and the smart grid,” working paper.
[7] K. W. Hedman, R. P. O’Neill, S. S. Oren, “Optimal transmission switching using Benders’ decomposition,” working paper.
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QUESTIONS?Thank you!
http://www.ieor.berkeley.edu/~oren/index.htm