Operation Scheduling of Power Systems with high ... · Operation Scheduling of Power Systems with...
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Operation Scheduling of Power Systems with high Penetration of Smart Grid Elements Fulbright Scholar Fellowship (host: Professor Arturo Bretas) Gambrinus Fellowship (host: Professor Christian Rehtanz) _____________ Sergio Rivera, PhD Fulbright Scholar University of Florida Associate Professor Universidad Nacional de Colombia email: [email protected] 1
Transcript of Operation Scheduling of Power Systems with high ... · Operation Scheduling of Power Systems with...
Operation Scheduling of Power Systems with high Penetration of Smart Grid Elements
Fulbright Scholar Fellowship(host: Professor Arturo Bretas)
Gambrinus Fellowship(host: Professor Christian Rehtanz)
_____________Sergio Rivera, PhDFulbright ScholarUniversity of FloridaAssociate ProfessorUniversidad Nacional de Colombiaemail: [email protected]
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Operation Scheduling of Power Systems with high Penetration of Smart Grid Elements
1. Operation scheduling of transmission systems with renewables penetration
Focus A. IEEE Optimization Competitions on Power Systems with Penetration of Renewable EnergiesFocus B. Uncertainty Cost Functions for Dispatchable Renewable Energy Systems
2. Operation scheduling of future distribution systems
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TraditionalOptimalPower Flow (OPF)
OPF consideringhigh penetration ofrenewables
OperationScheduling of Smart Grids
Security ConstraintsOptimal PowerFlow
2014 2017, 2018 2017, 2018, 2019 2019
IEEE PES GM IEEE PES GM IEEE ComputationalIntelligence Group
ARPA-E
GeneratorsOLTCShunts
GeneratorsRenewablesOLTCShunts
Smart Grids Elements GeneratorsContingencies
DELFTUNI-DUI
UNALGERSACCELOGIC
UNALUFGERS
GERSUNAL
Certification Certification 500 usd 4 M usd
SCHEDULING COMPETITIONS
1. Optimization Competitions on Power Systems with
Penetration of Renewable Energies
http://sites.ieee.org/psace-mho/
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1.1
0
1.2
1.1. 2017 CompetitionEvaluating the Performance of Modern Heuristic Optimizers on Smart Grid
Operation Problems
Stochastic OPF based active-reactive power dispatch
Sergio Rivera1, Andres Romero2, José Rueda3, Kwang Y. Lee4, István Erlich5
1Laboratorio de Metrología (LABE+i) and ElectroMagnetic Compatibility research group (EMC-UN), Department of Electrical Engineering, Universidad Nacional de Colombia, Bogotá, Colombia
2Instituto de Energía Eléctrica, Universidad Nacional de San Juan, San Juan, Argentina3Department of Electrical Sustainable Energy, Delft University of Technology, Delft, Netherlands
4Department of Electrical and Computer Engineering, Baylor University, Waco, USA5Electrical Power Systems, University Duisburg-Essen, Duisburg, Germany
[email protected], [email protected], [email protected], [email protected],
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Focus A. Optimization Competitions on Power Systems with
Penetration of Renewable Energies
0. 2014 competition
• OPTIMAL ACTIVE-REACTIVE POWER DISPATCH PROBLEM
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1.1. 2017 competition
• STOCHASTIC OPTIMAL ACTIVE-REACTIVE POWER DISPATCH PROBLEM
(with MC simulation)
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1.1. 2017 competition
• STOCHASTIC OPTIMAL ACTIVE-REACTIVE POWER DISPATCH PROBLEM
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Renewable Systems Scheduling
1.1. 2017 competition
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1.1. 2017 competition
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1.1. 2017 competition
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1.1. 2017 competition14
1.1. 2017 competition
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1.1. 2017 competition
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1.1. 2017 competition, wind generator case18
Weibull Distribution
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1.1. 2017 competition, solar generator case
Lognormal Distribution
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1.1. 2017 competition, small hydro generator case
Gumbel Distribution
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1.2. 2018 competition
• STOCHASTIC OPTIMAL ACTIVE-REACTIVE POWER DISPATCH PROBLEM
(without MC simulation)
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Uncertainty Cost Functions for
Dispatchable Renewable Energy
Systems
June, 2019
Probability of the primary source in a time instance
Electric Vehicles Charging demandWind Speed Solar Iradiance
μ=19,54
σ =0,54
σ =15,95
λ=6 y β=0,25
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Normal
Distribution
Rayleigh
Distribution
Log-normal
Distribution
June, 2019 Name
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PDF of theavailable
power in EV
f𝑊(Wav)
Penalty CostFunction
න𝐶 ∗ 𝑓𝑤(𝑊𝑎𝑣)UCF (EV)
UCF Electric Vehicles EV
June, 2019 Name
Results Montecarlo Wind
Wind Speed Histogram
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Cases Over=253399
26%
Cases Under
= 746601
74%
Scenarios
Scenarios
Scenarios
Scenarios
Power [MW]
June, 2019 Name
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Cases
Over=98458
98,45%
Cases
Under= 15154
01,54%
Scenarios
Scenarios
Scenarios
Scenarios
Results Montecarlo PV
June, 2019 Name
Comparison between Analytical and Montecarlo results
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2. Operation scheduling of future distribution systems
2.1 Optimization competitions of smart grids Operation
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2.1 Optimization Competition of smart grids operation
IEEE WCCI 2018 gathers 3 congresses and 13 competitions• 2018 International Joint Conference on Neural Networks• 2018 IEEE International Conference on Fuzzy Systems• 2018 IEEE Congress on Evolutionary Computation
GECCO 2019 gathers 2 congresses, in addition, the call is extended to the IEEE CEC 2019 and 8 competitions• 28th International Conference on Genetic Algorithms
(ICGA)• 24th Annual Genetic Programming Conference (GP)• IEEE Congress on Evolutionary Computation – CEC 2019 in
Wellington, New Zealand (CEC 2019)
2.1 Optimization Competition of smart grids operation
2.1 Optimization Competition of smart grids operation
ALGORITHM: VNS-DEEPSO Combination of Variable Neighborhood Search
algorithm (VNS) and Differential Evolutionary Particle Swarm Optimization
(DEEPSO)
2018
2019
2.1 Optimization Competition of smart grids operation
2.1.1 Considered Elements
2.1.1 Considered Elements
Smart grid7 renewables
and traditional
energy sources.
34 electric
vehicles.
2 energy
storage
systems .
90 controlable
loads (demand
response). 2 markets.
2.1.2 Considered aspects of uncertainty
Uncertaintyconsiderationsin smar grids
Load forecast
Planned EVs’ trips
Weatherconditions
Marketprices
2.1.3 Structure of the problem to solve
2.1.7 2019 Competition Results
Ranking Team Algorithm Ranking Index
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Universidad
Nacional de
Colombia,
ACCELOGIC y
Khalifa
University
VNS-DEEPSO 18,21
2Charusat,
Gujarat India
Enhanced Velocity
Differential
Evolutionary Particle
Swarm Optimization-
EVDEPSO
19,57
3University of
Porto
Chaotic Evolutionary
Swarm Optimization24,89
4Universidad de
Salamanca
Particle Swarm
Optimization with
Global Best
Perturbation PSO-GBP
31,02
5Charusat,
Gujarat India
Improved_Chaotic_Dif
ferential Evolution34,52
Ranking Algorithm Ranking Index
1 VNS-DEEPSO 63,95
2
Hybrid levy particle
swarm variable
neighborhood search
optimization
(HL_PS_VNSO)
84,10
3
Gauss Mapped Variable
Neighbourhood Particle
Swarm Optimization
(GM_VNPSO)
86,58
4 CUMDANCauchy-C1 113,03
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Particle Swarm
Optimization with Global
Best Perturbation (PSO-
GBP)
161,02
2018 2019
2.1.7 2019 Competition Results
2.1.7 2019 Competition Results
2.1.7 2019 Competition Results
2.1.7 2019 Competition Results
2.1.7 2019 Competition Results
2.1.7 2019 Competition Results
What is next?82
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* 2020 IEEE PES GENERAL MEETING COMPETITIONUncertainty in the Penalty Cost (outliers like ciber attacks)
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SCOPF Problem Formulation
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What is next?83
Funding to participate: -
Lines R&D
• Security Contraint OPF
• Power System Model Validation
• Asset Managment
Institute Location
Argonne National Laboratory Lemont, IL
Bigwood Systems, Inc. Ithaca, NY
Case Western Reserve University Cleveland, OH
Clemson University Clemson, SC
Georgia Institute of Technology Atlanta, GA
GERS USA, LLC Weston, FL
Lawrence Berkeley National Laboratory Berkeley, CA
Lawrence Livermore National Laboratory Livermore, CA
Lehigh University Bethlehem, PA
Institute Location
National Renewable Energy Laboratory Golden, CO
Northwestern University Evanston, IL
Pearl Street Technologies Pittsburgh, PA
The Optimization Firm, LLC Pittsburgh, PA
The Pennsylvania State University University Park, PA
University of California, Berkeley Berkeley, CA
University of Colorado, Boulder Boulder, CO
University of Texas at Arlington Arlington, TX
University of Utah Salt Lake City, UT
Operation Scheduling of Power Systems with high Penetration of Smart Grid Elements
1. Public Guest Lecture: operation scheduling of transmission systems with renewables penetration
Focus A. IEEE Optimization Competitions on Power Systems with Penetration of Renewable EnergiesFocus B. Uncertainty Cost Functions for Dispatchable Renewable Energy Systems
The operational planning of sustainable electrical power systems is facing higher stochasticity introduced by massive integration of variable renewable generation and the diversification of the sources for flexibility in highly interactive energy markets and multi-energy sector coupling. Therefore, the scheduling problems involved in operational planning need consideration of non-linear models, probabilistic models, and a large number of decision variables. This entails mathematically complex and computationally expense formulations, which cannot be tackled by classical optimization tools.
QUESTIONS [email protected]
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