Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Stochastic methods and models for optimising dynamic multi-objective problems
1. Stochastic methods and models
for multi-objective dynamic
optimisation problems
Eric S Fraga
Department of Chemical Engineering, UCL
University College London (UCL)
20 October 2017
Universidad de Salamanca
v1.39 23rd November 2017
2. 2 Dynamics http://www.ucl.ac.uk/~ucecesf/
Acknowledgements
The following did the actual work for the case studies
presented:
Dr Oluwamayowa Amusat, formerly Chemical
Engineering, UCL.
Mr Alistair Rodman, University of Edinburgh.
with inputs from Dr Dimitrios Gerogiorgis (Edinburgh IMP)
and Dr Paul Shearing (UCL Chemical Engineering).
Any errors, omissions or bloopers are mine and mine alone.
5. 5 Dynamics http://www.ucl.ac.uk/~ucecesf/
Definition (Stochastic)
adjective, Statistics.
1 of or relating to a process involving a randomly
determined sequence of observations each of which is
considered as a sample of one element from a probability
distribution.
6. 6 Dynamics http://www.ucl.ac.uk/~ucecesf/
Optimisation methods
Optimisation
methods
Deterministic Stochastic
Gradient
based
Direct
search
Steepest
descent
Dynamic
programming
Nelder
Mead
Hooke &
Jeeves
ESF & McKinnon, 2003
Whole
process
synthesis
ESF & Zilinskas, 2006
Integrated
process
with HENS
Nature
inspired
Genetic
algorithm
Ant
colony
Plant
propagation
Garrard & ESF, 1998
MENS
ESF & Rowe 2003
HENS
ESF & Amusat, 2016
Energy systems
(Strawberry)
Rodman, ESF & Gerogiorgis, 2017
Beer fermentation
(Strawberry)
8. 8 Dynamics http://www.ucl.ac.uk/~ucecesf/
Strawberry plant propagation algorithm
Given: f (x), ng , np, nr .
Output: z, set representing approximation to Pareto front.
1: p ← initial random population of size np
2: for ng generations do
3: prune population p, removing similar solutions
4: N ← fitness(p) rank based
5: ˜p ← φ
6: for i ← 1, . . . , np do
7: j ← select(p, N) fitness based
8: for k ← 1, . . . , nr ∝ Nj do runners
9: ˜p ← ˜p ∪ neighbour(xj , 1 − Nj ) propagation
10: p ← ˜p ∪ {xi |xi ∈ p ∧ f (xi ) nondominated} elitism
11: z ← {xi |xi ∈ p ∧ f (xi ) nondominated} Pareto set
A Salhi & ESF (2011), Proc. IceMATH K2–1–8.
9. 9 Fermentation http://www.ucl.ac.uk/~ucecesf/
Fermentation
essential step in manufacture of
alcoholic beverages.
responsible for alcohol content
and taste of final product.
process is temperature
controlled.
aim is to enhance alcohol
production, reduce time and
control flavours.
Source
Problem
What are the trade-offs between time and alcohol content
while constraining flavour affecting chemicals?
10. 10 Fermentation http://www.ucl.ac.uk/~ucecesf/
Optimisation problem
Solve
max
T(t)
z =
[EtOH]tf
−tf
, t ∈ [0, tf ]
where
T(t) temperature control profile,
tf fermentation time, and
[EtOH]tf
final ethanol concentration
subject to end-point constraints on concentration of
flavour affecting chemicals.
Concentrations of all species modelled by set of nonlinear
ordinary differential equations.
11. 11 Fermentation http://www.ucl.ac.uk/~ucecesf/
Temperature control profile
Strawberry requires a discrete representation of potential
solutions.
Representation must balance cover and efficiency for the
search.
Consider representation based on piecewise linear
temperature control profiles by specifying
1 initial temperature,
2 sequence of δt, ∆T values, and
3 final time interval duration.
with
δt ∈ [δtmin, δtmax] and δtmin > 0
∆T ∈ [∆Tmin, ∆Tmax]
based on engineering insight.
12. 12 Fermentation http://www.ucl.ac.uk/~ucecesf/
Example
x =
T0
11 40 3
δt1,∆T1
δt2,∆T2
60 −1 40 −1
δt3,∆T3
δt4
20
results in this piecewise linear temperature control profile:
Control profile
t T
0 11
40 14
100 13
140 12
160 12
19. 19 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Energy systems
Current energy systems
distributed geographically
but managed centrally
large scale power
generation
national and
trans-national power and
fuel transmission networks
distribution to users via
single branching networks.
Photo courtesy: Benkid77 Puddington-Shotwick footpath 27 110809
20. 20 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Local generation and distribution
Motivation
transmission losses
improved micro-generation
technologies
energy storage advances
need for off-grid operation
peak demand management
Examples include single buildings, neighbourhoods and large
scale possibly off-grid industrial operations.
21. 21 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Technologies I
Generation
µ-CHP
solar photovoltaic (PV)
solar thermal
wind turbines
for power and heat.
22. 22 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Technologies II
Storage
batteries
compressed air
hydraulic
molten salts
23. 23 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Energy network design
Must handle variability of generation and of demand:
dynamic modelling is necessary;
need to consider cyclic behaviour, e.g. periodicity of
demand or generation;
multiple technologies may be required;
is grid connection (electricity, gas) necessary?
⇒ models are differential, combinatorial and nonlinear.
24. 24 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Continuous mining operations
Olympic Dam, BHP Billiton, South Australia
26. 26 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Sustainable off-grid operation
Large scale, both thermal
and power.
Continuous operation for
many years.
Currently meet demands
using transported diesel
fuel.
Use renewable energy
Space not a constraint!
Question
What is the best integrated renewable energy system design
including energy storage?
31. 31 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Integrated system
Photo-
voltaics
Heat
Power
Power
Tower
Pumped Hydro Energy
Storage (PHES)
Advanced Adiabatic
Compressed Air Energy
Storage (AA-CAES)
Molten salt Tank Storage
(MTS)
Heat
Electricity direct to plant
GENERATION
DEMAND
Wind
Turbine
Power
block
Electricity
Heat
Vanadium Redox Flow
Battery System (VRFBS)
32. 32 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Energy generation
Sample equations:
photovoltaic
.
E
gen
PV (t) = ηpv (t)ηinv Ap
.
G
tot
(t)
wind
.
E
gen
wind (t) =
1
2
ηwtρAwtν3
(t)
storage ρcpVTES
d
dt
TTES
(t) = . . .
Resulting model is an initial value differential algebraic
system for t ∈ [0, 8760] h.
Quantities in red are variable and unpredictable.
33. 33 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Variability & Predictability
Climate is what you expect, weather is what you get.
Robert A. Heinlein (1973), Time Enough For Love.
34. 34 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Data modelling: collection
For each variable input (solar irradiance, wind speed):
1 Given hourly data for every day of every month over a set
of years, Dy,m,d,h, y = 1, . . . , ny , combine these data into
12 × 24 sets, one for each hour of a mythical day for each
month:
Dm,h ≡
ny
y=1
nd,m
d=1
Dy,m,d,h
for m = 1, . . . , 12
h = 1, . . . , 24
where Dy,m,d,h is a data point for year y, month m, day d
and hour h, and nd,m is number of days in month m.
35. 35 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Data modelling: model creation
2 Identify suitable statistical parameters, mean, standard
deviation, skewness and kurtosis, for best fitting
stochastic models:
pm,h = probability distribution function(Dm,h)
for m = 1, . . . , 12
h = 1, . . . , 24
36. 36 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Data modelling: scenarios
3 A scenario is set of hourly data, {vm,d,h}, for a complete
year:
1: for m ← 1, . . . , 12 do
2: for h ← 1, . . . , 24 do
3: vm,1,h ← pearsrnd(pm,h)
4: for d ← 2, . . . , nm,d do
5: ˆv ← pearsrnd(pm,h)
6: vm,d,h ← wd vm,d−1,h + (1 − wd )ˆv
where wd ∈ [0, 1] provides problem specific balance
between climate and weather.
37. 37 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Stochastic model
38. 38 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Optimisation problem
Photo-
voltaics
Heat
Power
Power
Tower
Pumped Hydro Energy
Storage (PHES)
Advanced Adiabatic
Compressed Air Energy
Storage (AA-CAES)
Molten salt Tank Storage
(MTS)
Heat
Electricity direct to plant
GENERATION
DEMAND
Wind
Turbine
Power
block
Electricity
Heat
Vanadium Redox Flow
Battery System (VRFBS)
Solve
min
x,y
z =
C(x, y)
−R(x, y)
,
an MI(D)NLP problem, where
y are choices of technologies and operating scheme,
x are the sizes and capacities of the generation and
storage units,
C is the capital cost (generation, storage) and
R is the measure of the reliability of the system in
meeting the given demands for a number of randomly
generated scenarios.
subject to thermo-physical constraints.
39. 39 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Reliability estimation
Definition (Loss of power supply probability, LPSPm)
Given a set of scenarios, S = {si , i = 1, . . . , ns},
LPSPm =
|{si : Ri < R , i = 1, . . . , ns}|
ns
where R is a lower bound on the acceptable reliability.
40. 40 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Solver
Use NSGA-II1
:
Solar & wind
Population size npop 100 100
Generations ngen 300 300
Scenarios ns 300 1200
Process cores nproc 8 12
Wall clock time tcpu 108 299 h
with binary tournament selection, intermediate crossover
(0.75), and Gaussian mutation (0.1).
1
Deb (2000), Comput Methods Appl Mech Engng 186:311-338.
41. 41 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Results
1 . 0 0 . 8 0 . 6 0 . 4 0 . 2 0 . 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
R u n 1
R u n 2
R u n 3
Capitalcost(€million)
L P S P m
Amusat, Shearing & ESF (2017). Computers & Chemical Engineering 103:103–115.
43. 43 Off-grid mining operations http://www.ucl.ac.uk/~ucecesf/
Reliability versus cost
Stochastic data modelling enables a second criterion for the
design of large scale off-grid mining operations with renewable
energy.
44. 44 Conclusions http://www.ucl.ac.uk/~ucecesf/
Summary
Optimisation
methods
Deterministic Stochastic
Gradient
based
Direct
search
Steepest
descent
Dynamic
programming
Nelder
Mead
Hooke &
Jeeves
Nature
inspired
Genetic
algorithm
Ant
colony
Plant
propagation
Thanks to Dr Dimitrios Gerogiorgis, Mr Alistair Rodman (U of
Edinburgh); Professor Abdel Salhi (U of Essex); Dr Mayowa Amusat, Dr
Paul Shearing (UCL), Birse Charitable Trust Fellowship (UK), PRESSID
(Nigeria).
www.ucl.ac.uk/~ucecesf