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3rd Energy Informatics Conference - November 2014 - Zurich 
HEMS: A Home Energy Market Simulator 
Andrea Monacchi, Sergii ...
Motivation 
● Home Energy Management Systems 
○ Unifying solution for storage, generation, consumption 
○ Rationalization ...
Market-based control 
● Distributed self-interested agents in competition 
○ Preferences as private price model (local vie...
Agent-based modeling 
● Scenario description format 
○ Weather model: cloudiness timeserie 
○ Power grid connections: trut...
The generation model 
● Available production over time 
a. External time series of measured power data 
b. Simple battery ...
The operation model 
● Device signature 
■ sequence of n states σ, with σi = ❬Pi, di, χs❭ 
■ device start delay sensitivit...
The operation model 
● Usage model 
○ Time of use probability Puse 
■ Puse = 1 − (Phold)N , over N instants 
● Puse = 1 − ...
Extracting appliance usage models 
● GREEND dataset 
○ P @ 1Hz in 8 selected households in AT and IT for 1 year 
Example: ...
Extracting appliance usage models 
● Appliance Usage Model Manager (UMMA) 
Device usage model interface Model extraction M...
Learning Smart controllers 
(ψs/pmax) 
fully-meshed 
ANN 
(amin/pmax) 
posASK 
mtcdASK 
Hour 
Month 
Weekday 
τ 
Importanc...
Learning Smart controllers 
● Objective: minimizing costs and discomfort 
● Heuristic fitness function 
○ F = R + (δg ∗Igr...
Learning Smart controllers 
● Running costs 
Expenses for energy imported from the Grid 
*Values are normalised on device ...
Evolving smart controllers 
● Evolutionary computing: selection of the fittest 
For g generations or until a fitness f is ...
Early experiments 
● Uniform-price double auction with 1 sec alloc. 
● 250 generations NNGA with 80 candidates each
Results 
● Learned controllers 
○ Well perform given objective fitness function 
● Selected market mechanism: 
○ Perform w...
Conclusions & Future work 
● HEMS Modeling & Simulation tool 
○ Assisted design of energy trading behavior 
○ Part of the ...
Questions? 
Andrea Monacchi 
Smart Grid group 
Alpen-Adria Universität Klagenfurt 
E: andrea.monacchi@aau.at 
W: http://ww...
Double-side auctions 
● More balanced than monopsonistic and monopolistic 
○ Convergence to price equilibrium even with a ...
Related work 
● Studies 
○ Alam et Al. energy exchange to minimize storage 
○ Power matcher city (the netherlands) 
● Simu...
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HEMS: A Home Energy Market Simulator

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Stability issues in the electric power grid originate from the rising of renewable energy generation and the increasing number of electric vehicles. The uncertainty and the distributed nature of generation and consumption demand for optimal allocation of energy resources, which, in the absence of sufficient control reserve for power generation, can be achieved using demand-response. A price signal can be exploited to reflect the availability of energy. In this paper, market-based energy allocation solutions for small energy grids are discussed and implemented in a simulator, which is released for open use. Artificial neural network controllers for energy prosumers can be designed to minimize individual and overall running costs. This enables a better use of local energy production from renewable sources, while considering residents’ necessities to minimize discomfort.

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HEMS: A Home Energy Market Simulator

  1. 1. 3rd Energy Informatics Conference - November 2014 - Zurich HEMS: A Home Energy Market Simulator Andrea Monacchi, Sergii Zhevzhyk, Wilfried Elmenreich Institute of Networked and Embedded Systems / Lakeside Labs Alpen-Adria-Universität Klagenfurt
  2. 2. Motivation ● Home Energy Management Systems ○ Unifying solution for storage, generation, consumption ○ Rationalization of local energy resources ○ Residents in the loop: awareness and cost control ○ Especially interesting for rural off-grid communities (uGrids) ● Goal: aid smart controller design ○ Energy allocation as a market
  3. 3. Market-based control ● Distributed self-interested agents in competition ○ Preferences as private price model (local view) ○ Better reflects needs of multiple users in a community ○ Price proportional to demand and inverse to supply ○ Demand and production are not known in advance ● Double-sided auctions ○ BID and ASK offers ordered by price in shared orderbook ○ Shared best BID (H), best ASK (L) ○ NYSE spread reduction rule ○ k-pricing: p = k * bid.p + (1 - k ) * ask.p, k ∈ [0,1]
  4. 4. Agent-based modeling ● Scenario description format ○ Weather model: cloudiness timeserie ○ Power grid connections: truth-telling agents ■ price models: energy tariff & feed-in tariff ■ power availability & power capability models ○ Prosumers: profit-oriented agents A. Monacchi, S. Zhevzhyk, and W. Elmenreich. Home energy market simulator: The scenario definition format. Alpen-Adria Universität Klagenfurt, Technical Report, September 2014. unit price Production model Demand model Flexible or Inflexible service?
  5. 5. The generation model ● Available production over time a. External time series of measured power data b. Simple battery models c. Exploiting a generation model dependent on current weather M. Pöchacker, T. Khatib, and W. Elmenreich. The microgrid simulation tool RAPSim: description and case study. In Proc. of the 2014 IEEE Innovative Smart Grid Technologies (ISGT) Conference, Kuala Lumpur, Malaysia, May 2014. Photovoltaic plant: ● peak power ● efficiency ● latitude ● longitude ● height ● size getCurrentProduction(current_date, current_weather)
  6. 6. The operation model ● Device signature ■ sequence of n states σ, with σi = ❬Pi, di, χs❭ ■ device start delay sensitivity χb (secs.) ■ intra-state interruption sensitivity χi Example power profile: dishwasher Correct operation User discomfort Currently none Available for smart appliances, mined for legacy devices
  7. 7. The operation model ● Usage model ○ Time of use probability Puse ■ Puse = 1 − (Phold)N , over N instants ● Puse = 1 − (1 - ω*)N then ω* = 1 - root(1 - Puse , N) ■ device starting willingness ω* ∈ [0, 1] for specific time instant ○ Willingness decay λ to update Puse ■ Puse = Puse(1 − λ)n , with n completed operations ■ simple but not expressive enough ○ Can be extracted from consumption data!
  8. 8. Extracting appliance usage models ● GREEND dataset ○ P @ 1Hz in 8 selected households in AT and IT for 1 year Example: Coffee machine A. Monacchi, D. Egarter, W. Elmenreich, S. D’Alessandro, and A.M. Tonello. GREEND: an energy consumption dataset of households in Italy and Austria. in Proc. of IEEE Int. Conf. on Smart Grid Communications (SmartGridComm), Venice, Italy, Nov 2014.
  9. 9. Extracting appliance usage models ● Appliance Usage Model Manager (UMMA) Device usage model interface Model extraction Model assessment and exploitation Example model: BN A. Monacchi, S. Zhevzhyk, and W. Elmenreich. Home energy market simulator: The appliance usage model manager. Alpen-Adria Universität Klagenfurt, Technical Report, September 2014.
  10. 10. Learning Smart controllers (ψs/pmax) fully-meshed ANN (amin/pmax) posASK mtcdASK Hour Month Weekday τ Importance χl b /χb (ψb/pmax) (bmax/pmax) posBID mtcdBID Seller’s Context Trader’s Buyer’s p = 2 ∗ pmax ∗ poutput − pmax Offer Type ASK p < -pth NOOP -pth <= p <= pth BID p > pth Offer price trading tendency: τ ∈ [-1,+1], with -1.0 sell and +1.0 buy
  11. 11. Learning Smart controllers ● Objective: minimizing costs and discomfort ● Heuristic fitness function ○ F = R + (δg ∗Igrid) − C Demand qj t = qj t,s + qj t,o , ∀bj ∈ B Rationality pt j ≤ ψbj,∀bj ∈ B; pt i ≥ ψsi,∀si ∈ S Brokerage qj t − qi t ≥ 0, with bj = si , ∀bj ∈ B,∀si ∈ S Self-trading bθ ≠ sθ , ∀θ ∈ Θ Reward: utility delivered to users upon operation completion Income from fed into Grid Running costs Similar approach I.Fehervari and W.Elmenreich. Evolving neural network controllers for a team of self-organizing robots. Journal of Robotics, 2010.
  12. 12. Learning Smart controllers ● Running costs Expenses for energy imported from the Grid *Values are normalised on device parameters Discomfort from delayed start of operated flexible devices Discomfort from delayed start of intermediate states in operated multi-state flexible devices Discomfort from state interruption of operated flexible devices Penalty from violating the market policy: NYSE Inflexible demand/supply not allocated Penalty resulting from irrational trading (sensitivity & reservation price violations) Penalty from price not reflecting needs: according to operation and generation model δg, δb, δs, δi, δm, δl, δn, etc Penalty selection assigns objective importance and drives evolution: cumbersome!
  13. 13. Evolving smart controllers ● Evolutionary computing: selection of the fittest For g generations or until a fitness f is reached Combine successful solutions Explore solution space ● FREVO: framework for evolutionary computing http://frevo.sourceforge.net A. E. Eiben and J. E. Smith, Introduction to evolutionary computing. Springer, 2003. Rank candidates by fitness
  14. 14. Early experiments ● Uniform-price double auction with 1 sec alloc. ● 250 generations NNGA with 80 candidates each
  15. 15. Results ● Learned controllers ○ Well perform given objective fitness function ● Selected market mechanism: ○ Perform well, although might yield suboptimal allocations i. Commodity divisibility ii. Prevention of offers exceeding available supply ○ Bundling problem: I only want B if I also get A i. Resource re-allocation to solve “conflicts” ii. Conflict avoidance (self-org.) through additional sensory input iii. Combinatorial auction (WDP is NP-Complete and centralised)
  16. 16. Conclusions & Future work ● HEMS Modeling & Simulation tool ○ Assisted design of energy trading behavior ○ Part of the FREVO evolutionary framework ○ Built-in generation/load models ○ Testbed for market mechanisms & policies ■ Market solver uncoupled from abstract simulator loop ■ Easy to add agents for specific markets ● Implementing variant of clock auction
  17. 17. Questions? Andrea Monacchi Smart Grid group Alpen-Adria Universität Klagenfurt E: andrea.monacchi@aau.at W: http://wwwu.aau.at/amonacch
  18. 18. Double-side auctions ● More balanced than monopsonistic and monopolistic ○ Convergence to price equilibrium even with a few traders ● BID and ASK offers ○ Ordered by price in a shared order book ○ shared market status: best BID (H), best ASK (L) ○ Offer accepting policy: NYSE spread reduction rule ● Pricing mechanism ○ k-pricing: p = k * bid.p + (1 - k ) * ask.p, k ∈ [0,1] ○ DDA vs CDA vs UCDA
  19. 19. Related work ● Studies ○ Alam et Al. energy exchange to minimize storage ○ Power matcher city (the netherlands) ● Simulators ○ AMES Wholesale Power Market testbed ○ JASA: Java Auction Simulator ○ CATS: Combinatorial auction testbed ○ DRSim: demand response simulator

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