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:
3
•
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
7/20/2015
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.
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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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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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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