A Tutorial on Blockchainfor Microgrids: Securing Distributed ...smart-grids. Decentral: 7 Distributed

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  • A Tutorial on Blockchain for Microgrids: Securing Distributed Optimization

    Prof. Scott Moura

    Energy, Controls, & Applications Laboratory (eCAL) University of California, Berkeley

    Eric Munsing

  • 2

    Challenges: Intermittency, Variability, Quantity of DERs Actors: Consumers, Aggregators, Utilities, System Operators

    Motivation

  • 3

    Example: Renewable Energy Credits Issues: Since RECs are not physical, how do we know Sara actually has 5 RECs to sell?

    How do we verify the RECs are legitimate?

    Who tracks and manages REC transactions?

  • 4

    Background Fields and Relevant Tools

    Optimization & Control

    Power Systems

    Economics

    • Mathematical Programming

    • Optimization Theory

    • Statistical Learning

    • Optimal Power Flow

    • Electricity Mkt Design• Game Theory

    • Sequence and Planning

    • Blockchains

    • Strategic Bidding

    • Distributed Optimization

    • Regulation

    • Economic Dispatch

  • Distributed v. Decentralized

  • 6

    Network Topologies

    System Operator

    Aggregator

    Centralized

    Distributed

    Fully Decentralized

    Centralized: Pros: Simple formulation Cons: Limited privacy, slow computations

    Distributed: Pros: Fast computation, protect privacy Cons: High communication overhead

    Pros: Low communication overhead, islanding Cons: Increased vulnerability to cyberattack

    Fully- Decentral:

  • 7

    Distributed v. Decentralized

    Weaknesses of aggregator-based consensus: • Monopoly distortions and trust issues • Single point of failure/attack • Limited scalability

    Alternative: Decentralized optimization • Consensus with neighbors who share variables • Effective for sparsely connected problems

    Mota et al: “D-ADMM: A Communication-efficient distributed algorithm for separable optimization” IEEE Transactions on Signal Processing (2012)

  • 8

    Do you trust your neighbors?

    Challenges: • Identify compromised nodes • Reach stable operation despite

    compromised nodes

    Assumptions: Compromised nodes… • Can affect problem data/constraints • Can send spurious messages

    ? ? ?

    ?

    ?

    ? ?

    ?

    ?

    ✓ ✓ ✓

    ✓ ✓

  • 9

    Options for Securing Fully-Decentralized Optimization

    ✓ ☠ ✓

    Create database of state variables • Securely timestamp variables • Requires trusted database manager

  • 10

    Blockchain-Secured Decentralized Optimization

    Blockchain as decentralized record-of-trust: • Record variable updates • Run feasibility checks • Compute update step • Anything a centralized aggregator can do!

    …And more: • Transparent & auditable • Fully decentralized: robust to dropouts

    ✓ ☠ ✓

    ✓ ☠ ✓

    Abstraction:

  • 11

    New Technology: Blockchains

    M1 t

    MN t

    Block Bt Contents

    M2 t

    Header

    Block No.

    Timestamp

    Validator ID

    Hash of Contents:

    ϒ(B1 )

    B1 B0 M1

    0

    Bt Bt+1 ϒ(B0)

    ϒ(B0)

    ϒ(Bt−1)

    ϒ(Bt+1)ϒ(Bt )

    ϒ(Bt )

    ϒ(Bt−1)

    ϒ(Bt )

    Decentralized, transparent database • Consensus mechanism for state updates • Trustless • Guarantees of security + execution • Robust, scalable, stable

    Open Research Areas • Coordinating decentralized devices • Decentralized optimization • Trusted sensors and data networks

    Appendix

  • 12

    Why Blockchains?

    Aggregators are great if: • One entity owns everything • No incentives to cheat • Communication is reliable • Aggregator isn’t attacked

    Why Blockchains? • Benefits of decentralization • Strengths of central

    auditors

    Failure Mode Aggregator Fully-Decentralized Blockchain

    ADMM

    Infeasible Constraints ✓ X ✓ Noise-injection attack ✓ ✓ ✓

    Communication dropouts X

    ✓ ✓

    Monopoly distortions X ✓ ✓

  • 13

    Outline

    Background: What problem does blockchain solve?

    Security: Cybersecurity in decentralized and fully-decentralized optimization

    Coordination: Model trustless DER coordination respecting distribution constraints

    0

    1

    2

  • 14

    1 Decentralized DER Coordination

    Q: How do we address trust issues in decentralized coordination networks?

    Novelty • Use blockchains to secure distributed optimization

  • 15

    Blockchains and Decentralized Optimization

    Appendix

    Local Computation

    Verification Step

    Updated Blockchain

    State

    Local Computation, … ,

    Auditable Consensus

    Blockchains:

    Examples: Bitcoin, Ethereum, …

    Decentralized Optimization:

    Local Optimization

    Update Step

    Solution

    Local Optimization,…,

    Consensus

    Examples: ADMM, Dual Splitting, …

    Local Problem

    Verifiable Update Step

    Solution Stored on Blockchain

    Local Problem, … ,

    Auditable Consensus

    Combined Paradigm:

    Novel Paradigm

    Munsing, Mather, Moura: “Blockchains for Decentralized Optimization of Energy Resources in Microgrid Networks” CCTA 2017

  • 16

    Application: Coordinating Distributed Energy Resources

    Local Problem

    Verifiable Update Step

    Solution Stored on Blockchain

    Local Problem, … ,

    Auditable Consensus …

    ADMM Update (on Blockchain)

    Consensus: Day-ahead Schedule

    Local ProblemLocal Problem

    Munsing, Mather, Moura: “Blockchains for Decentralized Optimization of Energy Resources in Microgrid Networks” CCTA 2017

    • Private problem: Minimize private costs

    • Global variables: Voltage & power estimates

    • Update step (blockchain): ADMM average

    • Converge when agree on network state

  • 17

    Distribution Networks and Convex Relaxations of OPF

    Single-phase AC distribution flow equations:

    li � P 2i +Q

    2 i

    vi ()

    ������

    2Pi 2Qi

    li � vi

    ������ 2

     li + vi

    SOCP Relaxation:

    pi = Pi � X

    k2�(i)

    Pk + rili, i = 0, . . . , n

    qi = Qi � X

    k2�(i)

    Qk + xili, i = 0, . . . , n

    vi = v⇡(i) + 2(riPi + xiQi)� (r2i + x2i )li

    li = P 2i +Q

    2 i

    vi , i = 1, . . . , n

    Evs & Shapeable Loads1: Psi,min  ss(t)  Psi,max TX

    t=1

    ss(t) = E s i,demand

    ssi (t) = 0 8t = 1, . . . , ti,startby ssi (t) = 0 8t = ti,endby, . . . ,T

    Batteries1: sbi (t) = d

    b i (t)� cbi (t)

    Pbi,dis  dbi (t)  0  cbi (t)  Pbi,charge Eb,min  Eb(t)  Eb,max Ebi (t) = E

    b i (t� 1) + cbi (t)�th

    i,in

    � dbi (k)�t/h i,out

    (1 + ")Ebi (1)  Ebi (T)  (1� ")Ebi (1)

    Deferrable Loads2: sd = �d

    8t = 1 . . .T : 0  di(t� 1)  di(t) di(t)  ai(t) ai(t� ⇣)  d(t) di(t) 2 (0, 1)

    2Le Floch et al: “Plug-and-Play Model Predictive Control for Load Shaping and Voltage Control in Smart Grids,” IEEE Transactions on Smart Grid, to appear 3Chang, Alizadeh, Scaglione: “Coordinated Home Energy Management for Real-Time Power Balancing,” IEEE PES General Meeting, 2012

    1M.E. Baran and F.F. Wul: “Optimal Capacitor Placements on Radial Distribution Systems”, IEEE Transactions on Power Delivery, 1989

  • 18

    Formulation

    Decompose using ADMM: • Local variables: Local line flows and injections • Global variables: Network line flows and injections

    Appendix

    min

    TX

    t=1

    nX

    i=1

    Ci,t(s g i (t))

    s.t. power flow equations t = 1, . . . ,T

    and DER constraints, i = 1, . . . , n

    over sgi (t) 2 [s g i , s

    g i ], i = 0, . . . , n, t = 1, . . . ,T

    (Pi, Qi, li, vi)(t), i = 1, . . . , n, t = 1, . . . ,T

    Objective: Minimize Energy Cost

    Y. Wang, L. Wu, and S. Wang, “A Fully-Decentralized Consensus-Based ADMM Approach for DC-OPF With Demand Response,” IEEE TSGI, 2016. Q. Peng and S. H. Low, “Distributed Algorithm for Optimal Power Flow on an Unbalanced Radial Network,” 53rd CDC, 2015. P. Sulc, S. Backhaus, and M. Chertkov, “Optimal Distributed Control of Reactive Power Via the Alternating Direction Method of Multipliers,” IEEE TEC, 2014.

  • 19

    Decentralize with ADMM and deploy on blockchain

    repeat Pi: Private Optimization, compute locally

    Gather private constraints

    Compute x̃i and send to smart contract S1

    S1: ADMM Aggregator, on blockchain Update z

    if krkk2  ✏pri, kskk2  ✏dual then Compute final schedule and clearing prices

    Send schedule to S2

    end until krkk2  ✏pri, kskk2  ✏dual

    Mi: Each Smart Meter Record energy consumption

    Send time-stamped & signed consumption to S2