Distributed Optimization for Smart Grids Jose Rivera , Christoph - - PowerPoint PPT Presentation

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Distributed Optimization for Smart Grids Jose Rivera , Christoph - - PowerPoint PPT Presentation

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Technische Universitt Mnchen Distributed Optimization for Smart Grids Jose Rivera , Christoph Goebel, and Hans-Arno Jacobsen Smart Buildings and Smart Grids Dagstuhl Seminar, February 22 27 , 2015 Department of Computer Science Chair for

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Technische Universität München

Distributed Optimization for Smart Grids

Department of Computer Science Chair for Application and Middleware Systems (I13)

Jose Rivera, Christoph Goebel, and Hans-Arno Jacobsen

Smart Buildings and Smart Grids Dagstuhl Seminar, February 22 – 27 , 2015

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Technische Universität München

Scenario

  • Very large number of (new) devices
  • Smart grid allows control

Can we actively integrate them into power system operations in a scalable, efficient and reliable manner? This usually involves solving optimization problems

Department of Computer Science, Chair for Application & Middleware Systems

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[energiewende-sta.de]

[frauenhofer-esk]

[ionexusa.com] [smartgridsmartcity_com_au]

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Technische Universität München

Centralized optimization

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Data Computation

Control center (Aggregator) Devices 1. All data to one solver 2. Solver optimizes 3. Results are send back 4. Done! Challenging to scale up to large number of devices !

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Technische Universität München

Hierarchical optimization

Department of Computer Science, Chair for Application & Middleware Systems

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Data Computation

Control center (Aggregator) Devices Scales better than centralized, but offers suboptimal results 1. Each solver gets part

  • f the data

2. Each solver optimizes 3. Each solver sends result back 4. Done! Idea: Simplify problem by splitting or creating a hierarchy of problems

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Technische Universität München

Distributed optimization

Department of Computer Science, Chair for Application & Middleware Systems

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Data Computation

Aggregator coordinator Devices subproblems Scalable and optimal !

1. Each subproblem gets local data 2. Coordinator sends coordination signal 3. Each solver optimizes 4. Each solver sends result to coordinator 5. Coordinator updates coordination signal 6. If no convergence go to 2 7. Done!

Idea: Divide into subproblems and one coordinator

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Technische Universität München

An example: EV ADMM

  • General algorithmic framework for aggregator convex optimization

(use case: EV charging)

Department of Computer Science, Chair for Application & Middleware Systems

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General aggregator optimization problem

ADMM

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Technische Universität München

Department of Computer Science, Chair for Application & Middleware Systems

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Using distributed optimization for the smart grid

  • Some issues

– Efficiency: Can have expensive network and cpu usage* – Reliability: No results until convergence (important for communication delays)

  • Needed

– Killer app – Platform for simple formulation and deployment

*MapReduce/Bigtable for Distributed Optimization: http://videolectures.net/nipsworkshops2010_guestrin_kml/

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Technische Universität München

DOPS: Distributed Optimization Pub/Sub

Publish/Subscribe middleware for EV aggregation

  • Performance improvement

based on DOs characteristics

  • Communication with

coordinator is a bottleneck

  • Idea: In-network

computation

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In-network computation: 2 x faster distributed optimization!

www.msrg.org/padres/

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Reliability: EV charging control

Problem: Real-time capability of distributed

  • ptimization

Idea: Anytime algorithms Result: EV charging control can run in milliseconds instead of minutes

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Distributed optimization feasible on each iteration

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Technische Universität München

Overview: Distributed optimization for smart grids

  • High potential for the definition of control protocols and practicable

system-wide optimization

  • Currently it may introduce more problems than it solves
  • Becomes interesting when centralized approach starts to be

intractable

  • Suitable for planning tasks, but close-loop control remains a

challenge

  • A lot of theory but very limited actual implementation

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Crowdsourcing grid data

Collection Verification Inference Analysis

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EnergyMap.info

Transformer? Generator?

Expert in the loop

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Technische Universität München

References (1/2)

  • C. Goebel et al., “Energy Informatics,” Business & Information Systems Engineering,

2013. Matt Kraning, Eric Chu, Javad Lavaei and Stephen Boyd, "Dynamic Network Energy Management via Proximal Message Passing", Foundations and Trends in Optimization: Vol. 1: No. 2, pp 73-126. Boyd, Stephen, et al. "Distributed optimization and statistical learning via the alternating direction method of multipliers." Foundations and Trends in Machine Learning 3.1 (2011): 1-122.

  • L. Gan, U. Topcu, and S. Low. Optimal decentralized protocol for electric vehicle
  • charging. IEEE Transactions on Power Systems, 2013.

Omid Ardakanian, Catherine Rosenberg, and S. Keshav. 2013. Distributed control of electric vehicle charging. In Proceedings of the fourth international conference on Future energy systems (e-Energy '13). ACM, New York, NY, USA, 101-112.

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References (2/2)

  • J. Rivera, M. Jergler, A. Stoimenov, C. Goebel, H.-A. Jacobsen. Using

Publish/Subscribe Middleware for Distributed EV Charging Optimization. Conference on Energy Informatics, Zürich, Switzerland, 2014.

  • J. Rivera, H.-A. Jacobsen. A Distributed Anytime Algorithm for Network Utility

Maximization with Application to Real-time EV Charging Control. 53rd Conference

  • n Decision and Control (CDC2014), Los Angeles, USA. 2014.
  • J. Rivera, P. Wolfrum, S. Hirche, C. Goebel, and H.-A. Jacobsen. "Alternating

direction method of multipliers for decentralized electric vehicle charging control." In 52nd Conference on Decision and Control (CDC2013), Florence, Italy, 2013.

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Technische Universität München

Thank you, questions or comments …

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Jose Rivera j.rivera@tum.de

Contact

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Outline: Power system operations

What is the status quo? Why do we need to do things differently? Our contribution Searching for a killer-app Summary and future work

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Power system operations are complex

[gettyimages]

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Technische Universität München

Power system operations

Department of Computer Science, Chair for Application & Middleware Systems

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Monitoring

  • Status & Analog

Retrieval (SAR)

  • Network Model Builder

(NMB)

  • Scheduler Function

(SF)

  • State Estimation (SE)
  • Network Sensitivity

(NS)

Analysis

  • Dispatcher Power

Flow (DPF)

  • Security Analysis (SA)
  • Short Circuit Analysis

(SCA)

Operation enhancement

  • Optimal Power Flow

(OPF)

  • Security Constrained

Dispatch (SCD)

  • Voltage Stability

Analysis (VSA)

  • Thermal Security

Analysis (TSA)

  • Available

Transmission Capacity (ATC=VSA+TSA)

  • Equipment Outage

Scheduler (EOS)

  • Bad Topology

Detection (BTD)

  • Network Parameter

Update (NPU)

  • Network Modeling

Assistant (NMA)

Decision support

  • Interlocking with LF &

SA

  • Study data base
  • Network save cases

[G. Björkman, ABB]

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Technische Universität München

Power system operations & time scales

Real-Time Operations (msec – 10s of minutes)

  • Protection (msec)
  • Frequency governors (sec)
  • Automatic Generation Control (AGC) (seconds)
  • State estimation and contingency analysis

(minutes)

  • Economic dispatch (~15 minutes)

Operation planning (days - years)

  • Load forecasting – days (short term) to years

(long term)

  • Unit commitment (day ahead markets)
  • Maintenance planning (weeks - year)
  • Generation and transmission planning ( up to 25

years)

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Monitoring Analysis Operation enhancement Decision support

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Technische Universität München

In simple words

Power system operations = Optimization

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Methods

Conventional

  • ptimization

methods

Nonlinear Programming (NLP) Linear Programming (LP) Quadratic Programming (QP) Generalized reduced gradient method Newton method Network Flow Programming (NFP) Mixed - Integer Programming (MIP) Interior Point (IP) methods

Intelligence search methods

Neural Network (NN) Evolutionary Algorithms (EAs) Tabu Search (TS) Particle Swarm Optimization (PSO)

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Conventional is preferred

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Why not heuristics?

  • No guaranteed good results
  • Also…

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  • An aggregator bundles the services of many small

devices

  • Every cent counts
  • Need:

– Scalability – Optimality – Privacy Aggregator Co.

Aggregator problem as a killer app?

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Example: The EV charging problem

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Munich

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If we control EVs

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Munich

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Distributed vs centralized optimization

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We can scale up using Distributed Optimization (DO)

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What about 1 Mio EVs in Germany

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Germany

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In search of a ….

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killer app of DO in power systems

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What is the current killer app of DO?

Machine learning at Google scale

Training with massive amounts of data

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Large Scale Distributed Deep Networks Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng

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Technische Universität München

What is another current killer app of DO?

Optimization problems over large scale networks

– Lack of centralized coordinator or information access – Time-varying system characteristics

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Rate control in communication networks Wireless sensor networks: Data gathering/estimation/localization

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Bringing it all together

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Distribution networks Data in smart grids

Killer app: Combine big data and the distribution grid. Automation and control? Predictive analytics?

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Summary

  • Power system operations require scalable, efficient and

reliable optimization

  • Distributed optimization offers scalability
  • Progress towards efficiency and reliability for distributed
  • ptimization
  • Killer app still required

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Future work

Move towards a platform for DO tailored to power systems

  • peration problems

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Elastic MapReduce