Transcript Document

Real Time Energy Management in Smart Grid
Sunil Kumar Vuppala*,$
GN Srinivasa Prasanna*
*CSL Lab, IIITB, Bangalore; $Infosys Limited, Banglaore
D2-02_07
CIGRE Colloquium on Smart Grid, Mysore
Nov 13-15, 2013
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7/20/2015
Outline
 Introduction
 Objective of the work
 Snapshot of Smart grid in India
 Identification of gaps with challenges
 Architecture of smart energy management
 Symbols and assumptions
 Mathematical model
 Analysis of Results
 Practical Implementation
 Conclusions and future work
 References
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Introduction
 Advances in Wireless Sensor Network Technologies:
 Fine grain monitoring and control till appliance level
 Energy: Supply Demand mismatch, peak hours, renewable energy sources
 Sustainability: Reduce carbon footprint
 Smart Grid:
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•
A framework for combining the electrical
power infrastructure with modern digital
communication networks and information
technology (ICT).
•
Two way communication, customer
participation, renewable energies and
storage, operational efficiency, quality of
power, self healing
Sunil Kumar Vuppala, IIITB
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Energy Management - Demand response (DR)
 Energy management is the process of monitoring,
controlling, and conserving energy in a building or
city.
 Demand side management includes energy efficiency,
Flat rate
• Fixed charge only with user categorization
• No incentive for user to use less in peak time
demand response; long term activity.
 DR is to manage customer consumption of
electricity in response to supply conditions and the
demand for the electricity.
Slab rate
activity between Utility and
consumers. Voluntary response by customers to
price signals.
• Slab based pricing with user categorization
only
• Each block is charged differently but no gain
for user to save energy during peak hours
 Coordinated
 Social & Financial benefits: Avoided investment to
meet the peak period demand; CO2 reduction.
 Around 25% of power generation and 10% of
distribution activities are associated to meet peak
hour demand in USA which is roughly 400 hours
annually.
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Real Time Energy Management in Smart Grid
Price based*
• Time varying prices like RTP, TOU
• Gives flexibility but basic needs (must run
appliances) also charged high during peak
hours
• Curtailable, bidding programs
• Only limited flexibility to user
• High penalties for non adherence in DR
Incentive based* programs
* Pricing for DR Participation7/20/2015
Objective of the Work
The key contributions in our paper include:
 Modeling large scale smart grid system optimization problem
of Smart Energy Management and Demand Response with
energy comfort trade off and solution methods for such
models.
 Detailed level modeling perform appliances, containing
complex inter dependent appliance constraints.
 Simulation, implementation and analysis of the model, and
the results to measure the mutual advantages of consumers
and utility companies.
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Snapshot of Smart Grid in India
• India has installed generation capacity of 200GW+ and generated
855 TWh of electricity in 2011-12 with a shortfall of 10% (base load)
to 12% (peak load).
• The electricity consumption is estimated to grow at an average rate of
3.3% per year through 2035.
• Formulation of policies for mandatory Demand Response(DR)
infrastructure for all customers with load >1 MW by 2013, >500
kW by 2015, >100 kW by 2017 and >20 kW by 2020.
 Smart Grid vision of India:
•
•
•
•
No power cuts, enablement of “Prosumer”
Manage peak power, Demand Response, EV proliferation
Reduce T&D losses, Improve quality of supply
Integrate distributed renewables generation efficiently
 Smart meters installation, Time of Use(ToU) tariff – in progress
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Sunil Kumar Vuppala, IIITB
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Identification of Gaps with Challenges
 Matching demand with supply from appliance to grid level with fine
grain level of monitoring/sensing information
 Demand Response programs can be more interactive and
automatic.


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Uncertainty in price needs to be captured
Customer needs to play an important role - prosumer
Flattening the demand curve over a day – virtual peak hours
Increase the participation of customers in DR programs
Optimal operation schedule of appliances with individual priorities of
the customers is missing
 No near real time optimal algorithms exists
 Tractability of the problem is important
 Multi objective both at utility and consumer - Energy-comfort trade off
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Sunil Kumar Vuppala, IIITB
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Architecture of Smart Energy Management
 Formulation –
tractability
 conversion to LP
 Hierarchical
architecture
 to solve the problem
in real time
 to handle scalability
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Optimization problem
 Objective: Optimize Operational efficiency
 Consumer level: Minimize energy bill
 Utility level: Maximize profit/social welfare
 Constraints:
 Comfort level – maximum waiting time
 Emission constraints – limits (Minimize carbon credits)
 Appliance level of constraints (Reboundable and Non reboundable





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


appliances: individual n inter appliance)
Uncertainty min-max constraints:
Real time pricing with prediction and min-max limits
Local energy – minimum and maximum availability
Time constraints – service constraints (Start time and end time of job)
Demand constraints - minimum demand to be met; Ramp up, down limits within
an hour
Utilization constraints – energy limits over a day
Available storage limits
Fairness index
Sunil Kumar Vuppala, IIITB
7/20/2015
Symbols used in the Optimization
t
x a -Scheduling vector for timeslot ‘t’ and appliance ‘a’
w - Waiting cost parameter ; C- segment of consumers (C1, C2, C3)
N = Total number of consumers (Integer)
M = Total number of generation options (Storage, renewable)
(Integer)
P – power quantity (kW) ;
E – total energy quantity (kWh)
π – price;
d – demand; e- energy ;
Max/Min – Max/Min limits; lim – limit;
ac- appliance category;
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Mathematical formulation of the
optimization problem
 Minimize Energy Bill
(number of units * price of unit at time t) + (predicted number of units *
price predicted at time t)
Minimize
e
t
* (  c , ac ) 
t
 e~
t
t
t
* ~ c , ac
Variable to determine with the
t
schedule. et = sum of x
t
a
 At building and aggregate to campus
 Maximize profit: (Revenue – operating cost)
N
Maximize ( R  OC )  ( 
t
t
i 1
d
t
N
t
i

t
c , ac


i 1
~
M
t
d ~ c , ac )  
t
i
t
 Comfort level –
 cost of waiting with maximum waiting time
j 1
s
t
j
c
t
t
T
t
t
lim
  wa * xa  wa
a t 1
st
et
w a a  ...  w a a  a  A
t
w a  0 ;[ st a , et a ]
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Sunil Kumar Vuppala, IIITB
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Mathematical formulation of the
optimization problem
 Beta cost function –
t
(wa )
w (w ) 
t
a
 increasing convex function
 These functions help in minimizing the
Min
total waiting cost for fair scheduling of
appliances
 Emission constraints – limits
 Unit Emission Rate * (Power from possible
sources)* <= allowance for duration
(over a day / at any time)
1 
,  0
1 

 em
j
t
w (xa )
* ( P j )  em
max
j M


 t

t
t
t



,



c , ac
c , ac
c , ac 
 c , ac


 Uncertainty min-max constraints with
bounds [Ref: Berstimas, 2005 Robust
Optimization]



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
- Robustness parameter
applying strong duality
dual variables
Sunil Kumar Vuppala, IIITB
Min (   

,
t
c , ac

|
t
c , ac


^
t
c , ac
t
c , ac
|
 

t
c , ac
)
s .t .   c , ac   c , ac . e
t
t
  0 ;  c , ac  0
t
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t
Mathematical formulation of the
optimization problem
 Appliance level of constraints (individual n inter appliance):
 General constraint specification method for various appliances with validity/ invalidity specified
by the consumer.
 Example constraints can be: Appliance A cannot run along with appliance B; Appliance
types C and D should run together etc… which can be represented in a truth table form.
 From minimized Boolean SoP form using K-Map:
 n = number of product terms
 m = number of Boolean variables in the term
 for i=1 to n
m

t
za
[  z q | (1  z q )]  m   BM * y r
 .

q 1
n

yr  n  1
r 1
y r  { 0 ,1}

where BM is large number
 indicator
za 
t
a  ac 1

var iable
za  1
t
a  ac 2
of
t
xa
A’B+AB
’
 a  A ,  t  T , z a  { 0 ,1}
t
z 1  z 2  1  (  1000 * y 1 )
z 2  z 1  1  (  1000 * y 2 )
y1  y 2  1
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Real Time Energy Management in Smart Grid
y 1 , y 2 , z 1 , z 2  { 0 ,1}
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Extension of Robust optimization
 Convex polyhedral uncertainty sets used to specify many kinds of
future uncertainties
 Linear constraints that model microeconomic behavior
 Aggregates, Substitutive and Complementary behavior for energy
demand of appliances
 Some examples of substitutive constraints are:
 dem_a0 + dem_a1 + dem_a2 <= 50
 dem_a0 + dem_a1 + dem_a2 >= 10
 Complementary constraints are:
 dem_a0 - dem_a2 <= 15
 dem_a0 - dem_a2 >= 2
where dem_ax are uncertain demand variables
 More general formulation of Berstimas model
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Typical scenario of an area
 Scenario: 1000 customers (in 3 segments) each having 10 appliances (a=10000;
i=1000)
 Number of timeslots t = 24
 j=10 (supply side options)
 Total number of integer variables:
 Appliance constraints: Avg of 2 per customer: 20000 + 20000 = 40000
(Including indicator/control and auxiliary variables);
 ~40,000 integer variables
 Total number of real variables:
 e,d variables - (24*10000)*2
 Scheduling vectors (a,t), weightage - (24*10000), (24*10000)
 Pricing variables (a,ac,t) = (2*3*24)
 Dual variables (2*3*24)
 ~950,000 real variables
 Total ~million variables (1,000,000+)
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Results
Input Data:
 Demands – maximum hourly demand
Energy consumption profile for resindential
3.5
– 3kW minimum -0kW
 Minimum daily consumption 15kWh.
 Price data varying from INR 5-10 per
kWh
 Demand is entered for each hour of 0-3
Without our model
kwh
 Ramping up/down limit – 1kW/h
Energy Consumption with our moel
3
2.5
2
1.5
kW.
 Level of uncertainty value between 0 –
max timeslots.
1
0.5
 Appliance type1 cannot run along with
appliance type2; Appliance types 3 and
4 should run together.
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0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hour of the
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Insights from Results
 Different cases are considered in running our model in IBM Cplex 11.0 solver at each
time slot.
 Uniform pricing with fixed bounds – performance of the solution:
 Result of energy consumption is equal to maximum demand value in every time slot.
 No bias to any of the timeslot - demand is fulfilled within the requested timeslot.
 Price variations – go up and immediately go down and then go up – continuous
oscillation consecutively in the neighboring timeslots. Even energy consumption
scheduling is also oscillating within the 0 to maximum demand.
 Zero uncertainty in pricing case - Result of energy consumption is equal to maximum
demand value in every time slot. No bias to any of the timeslot.
 100% uncertainty in pricing case - It is observed that the demand is fulfilled in the
timeslots with lower price.
 Effect of vary ramp up, down limits - More chance for optimization if higher limits of
ramp/up down are allowed.
 The model was run for up to 4000 timeslots with 10000s’ of iterations in cases of 86400
variables.
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PLEMS - System Implementation setup
 Middleware to extract the data from
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the smart plugs
Smart plugs - both sensing and
actuation – extending the legacy
appliances to participate in energy
management
Identifying the consumption pattern at

each energy consumption point.
Curb the identified energy wastage
using tools such as smart plugs with
inputs from optimization

module
Make policies adaptable with
feedback loop in the system.

Grouping of the devices as per the
priority to take part in demand
response programs and manage energy
efficiently.
Real Time Energy Management in Smart Grid
The hardware set-up consists of SE plugs
(Z0E-MP1(a) and ZOE-MLC1(b) from
SimpleHomnet)
Programmable SE gateways (Connectport
X2 SE (c) from Digi International)
The SE gateway acts as a bridge between SE
network and IP based Ethernet network.
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Case studies at appliance level plug load
management – consumption patterns
Practical implementation
of smart energy
management in
enterprise building results
into up to 40% of energy
savings.
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Conclusions and Extension work
 Modelling of the energy management as a price-setting
demand-response mechanism is discussed.
 Initial results indicate that our model is practical and scalable
to an area wide smart grid.
 This model is giving mutual benefit among the consumer and
the utility companies with a flat demand curve during a day
and the automation of the energy management and demand
response.
 In future, we further develop this model to consider the
price and demand uncertainty, and add renewable energies
and continue large scale simulations.
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References:
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
[1] Conejo, A.J.; Morales, J.M.; Baringo, L, “Real Time Demand Response Model”, IEEE Transactions on Smart
Grid, Vol 1 no.3, pp: 236-242, Dec 2010

[2] D. Bertsimas and M. Sym, “The price of robustness,” Oper. Res., vol. 52, no. 1, pp. 35–53, 2004.

[3] D.Bertsimas and A.Thiele, “A Robust Optimization Approach to Supply Chain Management”, Operations
Research, 2003

[4] D. Bertsimas and M. Sym, “Robust discrete optimization and network flows,” Math. Program., Ser. B, vol. 98, no.
1–3, pp. 49–71, 2003.

[5] A. J. Conejo, J. Contreras, R. Espínola, and M. A. Plazas, “Forecasting electricity prices for a day-ahead poolbased electric energy market,” Int. J. Forecast., vol. 21, no. 3, pp. 435–462, 2005.

[6] Sunil K. Vuppala, GNS Prasanna, "Optimal Operational Schedule of Appliances with Energy Comfort Trade off
in Smart Grid", 44th ORSI conference, Kolkata, Jan, 2012.

[7] Sunil K. Vuppala, GNS Prasanna “Closed Loop Feedback System for Demand Response in Smart Grid”, PAC2013 Bangalore, Apr 2013

[8] Aswal A, Sunil K Vuppala and GNS Prasanna, "Demand response in smart grid systems", 25th EURO,
Lithuania, July 2012.

[9] Aswal, A., Prasanna, G. N. Srinivasa, “A Robust Approach to Inventory Optimization under Uncertainty”,
IAENG Transactions on Engineering Technologies Volume 3 - 2009
Real Time Energy Management in Smart Grid
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Thank You
Sunil Kumar Vuppala
[email protected]
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