Andrea Monacchi, Sergii Zhevzhyk, Wilfried ElmenreichInstitute of Networked and Embedded Systems / Lakeside Labs

Alpen-Adria-Universität Klagenfurt

HEMS: A Home Energy Market Simulator

3rd Energy Informatics Conference - November 2014 - Zurich

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

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]

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?

The generation model

● Available production over timea. External time series of measured power datab. Simple battery modelsc. 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)

● Device signature■ sequence of n states σ, with σi = ❬Pi, di, χ

s❭■ device start delay sensitivity χb (secs.)■ intra-state interruption sensitivity χi

The operation model

Example power profile: dishwasher

Available for smart appliances, mined for legacy devices

Currently none

User discomfort

Correct operation

● 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!

The operation model

Extracting appliance usage models

● GREEND dataset○ P @ 1Hz in 8 selected households in AT and IT for 1 year

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.

Example: Coffee machine

Extracting appliance usage models

● Appliance Usage Model Manager (UMMA)

A. Monacchi, S. Zhevzhyk, and W. Elmenreich. Home energy market simulator: The appliance usage model manager. Alpen-Adria Universität Klagenfurt, Technical Report, September 2014.

Device usage model interface Model extraction Model assessment and exploitation

Example model: BN

Learning Smart controllers(ψs/pmax)

fully-meshedANN

(amin/pmax) posASK mtcdASK

Hour Month Weekday

τImportance

χlb /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

Learning Smart controllers

● Objective: minimizing costs and discomfort

● Heuristic fitness function○ F = R + (δg ∗Igrid) − C

I.Fehervari and W.Elmenreich. Evolving neural network controllers for a team of self-organizing robots. Journal of Robotics, 2010.

Demand qjt = qj

t,s + qjt,o , ∀bj ∈ B

Rationality ptj ≤ ψbj,∀bj ∈ B; pt

i ≥ ψsi,∀si ∈ S

Brokerage qjt − 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

Learning Smart controllers

● Running costs Expenses for energy imported from the Grid

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!

*Values are normalised on device parameters

Evolving smart controllers

● Evolutionary computing: selection of the fittest

● FREVO: framework for evolutionary computinghttp://frevo.sourceforge.net

A. E. Eiben and J. E. Smith, Introduction to evolutionary computing. Springer, 2003.

Rank candidates by fitness

Explore solution space

Combine successful solutions

For g generations or until a fitness f is reached

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 well, although might yield suboptimal allocations

i. Commodity divisibilityii. Prevention of offers exceeding available supply

○ Bundling problem: I only want B if I also get Ai. Resource re-allocation to solve “conflicts”ii. Conflict avoidance (self-org.) through additional sensory inputiii. Combinatorial auction (WDP is NP-Complete and centralised)

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

Questions?

Andrea Monacchi

Smart Grid groupAlpen-Adria Universität Klagenfurt

E: andrea.monacchi@aau.atW: http://wwwu.aau.at/amonacch

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

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