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Consensus in Smart Grids for Decentralized Energy Management

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Talk in Multi-agent based Applications for Smart Grids and Sustainable Energy Systems Workshop (MASGES), in PAAMS '14 conference (SAlamanca, 2014) …

Talk in Multi-agent based Applications for Smart Grids and Sustainable Energy Systems Workshop (MASGES), in PAAMS '14 conference (SAlamanca, 2014)

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  • 1. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Consensus in Smart Grids for Decentralized Energy Management M. Rebollo C. Carrascosa A. Palomares Univ. Politècnica de València (Spain) MASGES ’14 Salamanca, June 2014 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 2. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Energy management problem Motivation New control mechanisms are needed for the near future power systems components connected in some network structure large scale → avoid information overload decentralized and distributed control mechanisms M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 3. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Our proposal The challenge Create a self-adaptive MAS that adapts itself to the electrical demand using local information. What is done. . . combination of gossip protocols to spread information to direct neighbors real-time adaption to changes in the demand failure tolerant M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 4. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Outline 1 Outline 2 Network characterization 3 Adaptive consensus-based distributed coordination mechanism 4 Adaption to demand 5 Adaption to failures 6 Conclusions M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 5. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Balearic Islands power grid 0 1 2 3 4 5 −0.5 0 0.5 1 1.5 2 2.5 Station Degree Distribution log(nodes) log(degree) 57 substations and 82 lines (30kV to 220kV) average degree = 2.8 diameter = 14 average path length = 4.7 clustering coef. = 0.33 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 6. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Centrality measures degree: node with more connections closeness: distance to the rest of the nodes betweenness: number of paths that uses the node eigenvector: links with other important nodes k-core: connected with nodes with degree ≥ k M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 7. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Consensus process 1. each node has an initial value 1 2 3 4 x1 = 0.4 x2 = 0.2 x3 = 0.3 x4 = 0.9 x1 = 0.4 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 8. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Consensus process 2. the value is passed to the neighbors 1 2 3 4 x1 = 0.4 x2 = 0.2 x3 = 0.3 x4 = 0.9 x1 = 0.4 x1 = 0.4 x1 = 0.4 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 9. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Consensus process 3. the values from the neighbors are received 1 2 3 4 x1 = 0.4 x2 = 0.2 x3 = 0.3 x4 = 0.9 x2 = 0.2 x4 = 0.9 x3 = 0.3 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 10. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Consensus process 4. the new value is calculated by x(t+1) = x(t)+ε j∈Ni [xj(t) − xi (t)] where ε < mini 1 di 1 2 3 4 x1 = 0.45 x2 = 0.425 x3 = 0.325 x4 = 0.6 x1 = 0.4 M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 11. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Data aggregation protocols consensus can not calculate aggregate values consensus belongs to a broader family of protocols network topology: unstructured routing scheme: gossip M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 12. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Push-Sum algorithm 1 {(ˆsr , ˆwr )} the pairs received by i at step t − 1 2 si (t) ← r ˆsr 3 wi (t) ← r ˆwr 4 a target fi (t) is chosen randomly 5 1 2 si (t), 1 2 wi (t) is sent to fi (t) and to i (itself) 6 si (t) wi (t) is the value calculated for step t M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 13. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Push-Sum formulation si (t+1) = si (t) di + 1 + j∈Ni sj(t) dj + 1 , wi (t+1) = wi (t) di + 1 + j∈Ni wj(t) dj + 1 where di is the number of neighbors of agent i (degree of i). si (t)/wi (t) converges to lim t→∞ si (t) wi (t) = i si (0) when wi (0) = 1 ∀i. M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 14. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Combination of Push-Sum and consensus gossip is used to 1 determine the number of active substations 2 calculate the total capacity of the network consensus is used to adjust the total demand (follow the leader) M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 15. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Energy pattern M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 16. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to the demand 0 50 100 150 0 100 200 300 400 500 600 700 Adaption to the Demand #epoch demand(MWh) cummulated demand M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 17. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to the demand 0 50 100 150 0 100 200 300 400 500 600 700 Adaption to the Demand #epoch demand(MWh) cummulated demand M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 18. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to the demand 0 50 100 150 0 100 200 300 400 500 600 700 Adaption to the Demand #epoch demand(MWh) cummulated demand 50 55 60 65 70 580 590 600 610 620 630 640 650 660 Adaption to the Demand (zoom) #epoch demand(MWh) M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 19. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to the demand 0 50 100 150 0 100 200 300 400 500 600 700 Adaption to the Demand #epoch demand(MWh) cummulated demand 50 55 60 65 70 580 590 600 610 620 630 640 650 660 Adaption to the Demand (zoom) #epoch demand(MWh) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 400 500 600 700 Adaption to the Demand (2 weeks) #epoch demand(MWh) cummulated demand M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 20. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Evolution of the relative error 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −0.04 −0.02 0 0.02 0.04 %error #epoch Evolution of the relative error 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −0.04 −0.02 0 0.02 0.04 Evolution of the relative error adapting to a random demand #epoch %error M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 21. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to failures 350 375 400 425 450 5800 6000 6200 6400 6600 6800 7000 #epochs errorrate Evolution after a change in the demand M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 22. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to failures 350 375 400 425 450 5800 6000 6200 6400 6600 6800 7000 #epochs errorrate Evolution after a change in the demand 350 400 450 500 550 1.38 1.4 1.42 1.44 1.46 1.48 1.5 x 10 4 #epochs errorrate Evolution after the failure of one substation M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 23. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Adaption to failures 200 400 600 800 1000 1200 1400 1600 1800 2000 −20 −10 0 10 20 #epochs errorrate Comparitions of the evolution of the error rate (Llucmajor substation failure) no failures substat fail difference M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management
  • 24. Outline Network characterization ADCDA Adaption to demand Adaption to failures Conclusions Conclusions What we’ve done To apply a combination of gossip methods to create a failure tolerant, self-adaptive MAS that manages an electrical network information exchanged with direct neighbors only no global repository of data nor central control needed push-sum and consensus protocol combined the network adapts itself to changes in the electrical demand failures are detected and assumed by the rest of active substations M. Rebollo et al. (UPV) MASGES’14 Consensus in Smart Grids for Decentralized Energy Management