Vashti Galpin presents a prototype model of a residential smart energy scheme using stochastic Hybrid Process Equations (HYPE) to model energy flow between households. The model represents four connected households that each have a wind turbine and electric vehicle. Experiments compare the total cost and energy usage under different sharing policies. Results show sharing unused renewable energy between households improves efficiency and reduces costs compared to not sharing. Extensions to model additional energy sources, storage and sharing policies at larger scales are discussed.

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Modelling residential smart energy schemes
Vashti Galpin
Laboratory for Foundations of Computer Science
School of Informatics, University of Edinburgh
2nd FoCAS Workshop on Fundamentals of
Collective Adaptive Systems
8 September 2014
FoCAS 2014 1 / 22

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Neighbourhood energy scheme
Oviedo et al, IEEE PES Transmission & Distribution Conference and Exposition, 2012
Oviedo et al, International Journal of Electrical Power & Energy Systems, 2014
FoCAS 2014 2 / 22

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Neighbourhood energy scheme
Oviedo et al, IEEE PES Transmission & Distribution Conference and Exposition, 2012
Oviedo et al, International Journal of Electrical Power & Energy Systems, 2014
FoCAS 2014 2 / 22

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Neighbourhood energy scheme
Oviedo et al, IEEE PES Transmission & Distribution Conference and Exposition, 2012
Oviedo et al, International Journal of Electrical Power & Energy Systems, 2014
FoCAS 2014 2 / 22

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Neighbourhood energy scheme
Oviedo et al, IEEE PES Transmission & Distribution Conference and Exposition, 2012
Oviedo et al, International Journal of Electrical Power & Energy Systems, 2014
FoCAS 2014 2 / 22

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Suburb energy scheme
FoCAS 2014 3 / 22

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Suburb energy scheme
FoCAS 2014 3 / 22

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Suburb energy scheme
FoCAS 2014 3 / 22

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Suburb energy scheme
FoCAS 2014 4 / 22

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Suburb energy scheme
FoCAS 2014 5 / 22

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Suburb energy scheme
FoCAS 2014 5 / 22

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Suburb energy scheme
FoCAS 2014 5 / 22

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Suburb energy scheme
FoCAS 2014 6 / 22

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Suburb energy scheme
FoCAS 2014 7 / 22

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Outline
1 Motivation and goals
2 Stochastic HYPE
3 Prototype model
4 Results
5 Extensions
6 Conclusions
FoCAS 2014 8 / 22

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Motivation and goals
QUANTICOL
Quantitative modelling of collective adaptive systems (CAS)
Scalable, population-based quantitative dynamic modelling
Abstraction and
uid and mean-

17. eld approximations
Formal language for description of CAS
Modelling to reason about existing and potential systems
Case studies from smart transport and smart grids
Spatial aspects are crucial to case studies
Investigation of existing quanti

18. ed spatial approaches
Development of appropriate modelling techniques
Design work
ow for quantitative modelling of CAS
FoCAS 2014 9 / 22

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Motivation and goals (continued)
Residential smart grids
Local generation of renewable energy plus grid supply
Collective: multiple households with various characteristics
together with communication between them
Adaptive: responds to information about prices and unused
renewable energy, policies are necessary
Prototype model to explore and understand features of scenario
Use of an expressive formalism (stochastic HYPE) with an
existing simulation tool (SimHyA)
Spatial aspects in the case of multiple neighbourhoods
Application of spatial moment closure techniques
FoCAS 2014 10 / 22

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Stochastic HYPE
Stochastic hybrid process algebra
Continuous, stochastic and instantaneous behaviour
Process algebra: small, elegant, formal language to describe
concurrent behaviour with mathematical semantics
Stochastic HYPE is very expressive, simulation for analysis
Flows of energy are represented as continuous behaviour
Availability of renewable energy and absence of vehicle can be
expressed by stochastic durations
Changes of policy and changes in energy
ows can be captured
through conditions for instantaneous events
FoCAS 2014 11 / 22

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Prototype model
neighbourhood: four houses in a circle linked to neighbours
household: wind turbine, plug-in hybrid electric vehicle (PHEV)
electricity has three prices: peak, mid-peak, o-peak
battery charging policy
if wind turbine generating then charge PHEV battery
if battery below threshold then charge from grid
if battery above threshold and not peak then charge from grid
energy sharing policy
if wind turbine generating and battery is full or absent
then oer energy to connected neighbour
FoCAS 2014 12 / 22

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Prototype model (continued)
weekday behaviour only
wind: exponential distributions for presence and absence of
sucient wind to generate energy to match UK average
departure of PHEV: exponentially distributed time after 7am
return of PHEV: exponentially distributed time after 4pm
distance travelled: exponential distribution with average 40 km
battery parameters: from actual vehicle
wind turbine parameters: from actual turbine
FoCAS 2014 13 / 22

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Experiments and results
three experimental conditions, total cost and usage over 20 days
no generation of energy by wind
wind generation without sharing
wind generation with sharing
eciency measures
cost eciency: ratio of savings to costs without renewables
energy eciency: ratio of renewable energy to all energy
wind eciency: ration of wind used to wind available
results with battery threshold of 4 kWh for initial charge
no wind no sharing sharing
cost $106.51 $64.19 $47.67
cost eciency 40% 55%
energy eciency 30% 38%
wind eciency 48% 62%
FoCAS 2014 14 / 22

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Results: energy from grid
800
700
600
500
400
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Energy from grid
Threshold
No wind energy
Wind energy without sharing
Wind energy with sharing
(average of 50 simulations of a 20-day period)
FoCAS 2014 15 / 22

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Results: cost of energy from grid
200
180
160
140
120
100
80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Cost of energy from grid
Threshold
No wind energy
Wind energy without sharing
Wind energy with sharing
(average of 50 simulations of a 20-day period)
FoCAS 2014 16 / 22

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Results: eciency
100
90
80
70
60
50
40
30
20
10
0
Energy efficiency, WES
Cost efficiency, WES
Wind efficiency, WES
Energy efficiency, WEN
Cost efficiency, WEN
Wind efficiency, WEN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Threshold
(average of 50 simulations of a 20-day period)
FoCAS 2014 17 / 22

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Extensions
addition of appliances to enable better use of renewables in
household
addition of household battery to enable better storage of
renewables in household
addition of photovoltaic panels to use solar energy
limitation about number of batteries being charged at peak
periods
neighbourhood wind turbine rather than household wind turbines
possibility of returning extra power to grid
policies:

28. rst-come,

29. rst served or something else?
FoCAS 2014 18 / 22

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Extensions (continued)
more formal treatment of various energy sources and storage
HR household renewables
ND household connection to neighbourhood distribution point
GR neighbourhood connection to national grid
FS household

31. xed storage
MS household mobile storage
AP household appliances
NR neighbourhood renewables
NS neighbourhood

32. xed storage
CN connections to other neighbourhoods
FoCAS 2014 19 / 22

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Extensions (continued)
quantitative description of energy
ows
HR ND GR FS MS AP
HR X [0,1] [0,1] [0,1] [0,1] [0,1]
ND X X [0,1] [0,1] [0,1] [0,1]
GR X X X [0,1] [0,1] f0,1g
FS X X X X [0,1] [0,1]
MS X X X X X [0,1]
AP X X X X X X
separation of concerns: physical events versus policies
scalable modelling: suburb of many neighbourhoods
FoCAS 2014 20 / 22

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Conclusion
Prototype model of residential smart energy scheme
Simulation to explore scenario and compare policies
Going forward with extensions to the model
Scalable model of many neighbourhoods with spatial variation
Fluid, hybrid and system-of-systems techniques
FoCAS 2014 21 / 22

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Thank you
FoCAS 2014 22 / 22