On the Concept of Multi-Period Flexibility
From Home Energy Management Systems……
Rui Pinto, Ricardo Bessa, Manuel Matos
Centre for Power and Energy Systems - INESC TEC
October, 2016
Research and Technological Development | Technology Transfer and Valorisation | Advanced Training | Consulting
Pre-incubation of Technology-based Companies
Agenda
• Introduction
• Methodology
–
–
–
–
Algorithm Flowchart
Trajectory Construction Process
Learning Feasibility Domain
Validating Trajectories
• Preliminary Results
• Conclusion and Future Work
27/10/2016
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Networks to Smart Grids, Coimbra
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Introduction
• DSO with technical difficulties in LV and MV distribution
grid – deep penetration of DRES
– Bus voltage limits
– Power imbalances
• Microgrids are valuable assets providing flexibility during
stressful events
• HEMS smartly manage the home’s flexible loads (AC, EWH)
and energy storage units, based on received information:
load forecast, microgeneration forecast, electricity price
forecast
• This work presents an algorithm to model the multitemporal flexibility that HEMS can provide
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Methodology
• HEMS must account for house static demand, PV forecast,
expected hot water demand, SoC of storage unit,
temperature of EWH water
• Trajectory: set of power set-points along a determined
period of time where the HEMS indicates the flexibility it
can provide
• House demand, PV generation, SoC of storage unit,
temperature of EWH impose constraints in the flexibility
modeling problem
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Methodology
• Example: battery with 6.4 kWh capacity, maximum
charge/discharge power 3.3 kW, initial SoC 1.28 kWh, SoC
[0.96, 6.4] kWh
• [1.28, 4.28, 7.28, 7.28]
kWh
• Flexibility
visual
representation becomes
impossible for problems
with more than 3 time
steps modeling
27/10/2016
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Methodology – Algorithm Flowchart
• Sampling routines are used to
generate feasible trajectories
complying with defined constraints
Trajectory Construction Process
Stage2:
Customer’s
Preferences
Validation
Stage1:
Trajectories
Generation
Set of Feasible Trajectories
• Set of feasible trajectories feeds a
SVDD function that learns the
flexibility domain
Learning Flexible
Domain Process
SVDD
Support Vector
Data
Description
Support Vectors
• Originated support vectors can be
used by DSO to verify and correctly
schedule its flexible assets in order
to meet operational criteria
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Validation of Multi-period Flexibility
Flexibility
Needs
1
DSO
3
HEMS
Flexibility
2
Validation of required
flexibility provision
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Methodology – Trajectory Construction Process
• Decision variable, Pbat, representing battery power flow
• Decision variable, Pewh, representing operating status of
EWH
• Control variables representing the battery SoC and the
temperature of EWH water
• First stage: random sampling routine creates trajectories
providing diversity in the originated set. A trajectory results
of the sum of the decision variables for all time steps
𝑡𝑟𝑎𝑗ℎ = 𝑃𝑏𝑎𝑡ℎ + 𝑃𝑒𝑤ℎℎ
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Methodology – Trajectory Construction Process
• For all time steps, the decision variables values must be
ranged between the defined equipment technical limits
𝑃𝑏𝑎𝑡 𝑚𝑖𝑛 ≤ 𝑃𝑏𝑎𝑡ℎ ≤ 𝑃𝑏𝑎𝑡 𝑚𝑎𝑥
0
, 𝑓𝑜𝑟 off 𝑠𝑡𝑎𝑡𝑢𝑠
𝑃𝑒𝑤ℎℎ =
𝑃𝑒𝑤ℎ𝑛𝑜𝑚 , 𝑓𝑜𝑟 on 𝑠𝑡𝑎𝑡𝑢𝑠
• Control variables are updated and problem constraints are
evaluated
𝐻
𝑆𝑜𝐶 𝑚𝑖𝑛 ≤ 𝑆𝑜𝐶 𝑖𝑛𝑖 +
𝜃 𝑚𝑖𝑛
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𝑃𝑏𝑎𝑡ℎ ≤ 𝑆𝑜𝐶 𝑚𝑎𝑥
ℎ=1
≤ 𝜃ℎ ≤ 𝜃 𝑚𝑎𝑥
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Methodology – Trajectory Construction Process
∆𝑡
−𝛼 𝜃ℎ−1 − 𝜃ℎ𝑜𝑢𝑠𝑒 − 𝑐𝑝 𝑣ℎ 𝜃𝑑𝑒𝑠 − 𝜃𝑖𝑛𝑙 + 𝑃𝑒𝑤ℎℎ
𝐶
• If problem constraints are not being respected, algorithm
modifies the decision variables values. If some constraint
remains not respected, trajectory is considered not
feasible and is rejected
• Second stage: the customer’s preferences are evaluated.
The main use of battery must be to accommodate PV
generation surplus that occurs at the times when PV
generation is superior to the house static demand.
Maximum physically possible amount of PV surplus energy
that the battery can absorb without flexibility provision
must still be assured when defining feasible trajectories
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Methodology – Trajectory Construction Process
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Methodology – Learning Feasibility Domain
• Final set of feasible trajectories is used as input in a
Support Vector Data Description function, namely a OneClass Support Vector Machine (SVM)
• Created model learns and delimits the feasibility domain,
identifying the necessary support vectors
• Some trajectories are considered
indispensable
to
delimit
the
feasibility boundary – support vectors
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Methodology – Validating Trajectories
• The radius of the high-dimension sphere delimiting the
feasibility boundary comes from:
𝑅2 𝑥 = 1 − 2
𝛽𝑖 𝑘 𝑥𝑖 , 𝑥 +
𝑖
𝛽𝑖 𝛽𝑗 𝑘 𝑥𝑖 , 𝑥𝑗
𝑖,𝑗
• 𝑥𝑖 , 𝑥𝑗 are support vectors and 𝑥 is the trajectory being
evaluated. Using a support vector as 𝑥 gives the sphere’s
radius
• Trajectories leading to radius smaller or equal to the
sphere’s radius are classified as feasible
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Preliminary Results
• Efficiency results on evaluating original set of feasible
trajectories
Kernel type
Rbf
Poly
Sigmoid
Rbf
Poly
Sigmoid
Rbf
Poly
Sigmoid
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# of correct evaluations
# of incorrect evaluations
γ = 0.05
nu = 0.1
1187
1185
1188
132
134
131
γ = 0.5
nu = 0.1
1044
1185
0
275
134
1319
γ = 0.05
nu = 0.01
1299
1290
1302
20
29
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Error
10.001 %
10.156 %
9.932 %
20.849 %
10.156 %
100 %
1.516 %
2.199 %
1.289 %
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Conclusion and Future Work
• There is great value for DSO and demand/flexibility
aggregators in having reliable and smart information
regarding flexibility provision
• Defining HEMS multi-temporal flexibility domain comes
with great effort, specially when simultaneously
accounting for microgeneration, energy storage equipment
and EWH constraints, and modeling of customer
preferences
• Proposed algorithm is able of efficiently define feasibility
domain and evaluate whether trajectories are feasible or
not – more complete efficiency assessment is required
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Conclusion and Future Work
• Uncertainty is becoming indispensable for power system
research works, specially when focusing in LV and MV
distribution grids
• Electric power infrastructures are more and more coupled
to generation units dependent on weather and climate
conditions increasing uncertainty in decision making
processes
• Future algorithm version will produce robust trajectories
(feasible for several scenarios) and use them to define the
feasibility domain
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Thank you for your attention!
Rui Pinto
[email protected]
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Methodology – EWH modeling challenges
• EWH modeling challenges: tricking the model
• Feasibility
Discontinuity
caused by discrete
EWH operation
• Most
times
battery can adjust
its output
• EWH close to
continuous power
output modeling
solves this issue
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∆𝑡
−𝛼 𝜃ℎ−1 − 𝜃ℎ𝑜𝑢𝑠𝑒 − 𝑐𝑝 𝑣ℎ 𝜃𝑑𝑒𝑠 − 𝜃𝑖𝑛𝑙 + 𝑃𝑒𝑤ℎℎ
𝐶
Where:
∆𝑡 is the time step [h];
𝐶 is the thermal capacity [kWh/ºC] = 0.117
𝛼 is the thermal admittance [kW/ºC] = -9.42-4
𝜃ℎ𝑜𝑢𝑠𝑒 is the indoor temperature = 20 ºC
𝑐𝑝 is the water specific heat [kWh/(ltr.ºC]
𝑣ℎ is the hot water consumption volume
𝜃𝑑𝑒𝑠 is the desired water temperature for consumption = 38 ºC
𝜃𝑖𝑛𝑙 is the inlet water temperature = 17 ºC
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