The project involves determining real time electricity charges incurred by the residential consumers. The smart grid integrated with residential PV systems was modeled in Simulink to determine demand response in dynamic pricing environment. Based on the load demand, electricity charges were calculated and compared with flat rate charges to highlight cost savings.
22CYT12 & Chemistry for Computer Systems_Unit-II-Corrosion & its Control Meth...
Real Time Pricing Simulator for Smart Grids
1. Real Time Pricing Simulator
for a Smart Grid
Swantika Dhundia
Power System Operation and Control
2. 2
22
Index
• Motivation (Slide 3)
• Challenges (Slide 4-5)
• Electricity Pricing (Slide 6-9)
• Literature Review (Slide 10-12)
• Big Idea and Objective (Slide 13)
• Proposed Method Details (Slide 14-21)
• Simulation Implementation (Slide 22-23)
• Simulation Results (Slide 24-27)
• Advantages of the Proposed Method (Slide 28-29)
• Limitations and Possibilities (Slide 30-31)
• Conclusions (Slide 32)
• References (Slide 33-34)
3. 3
33
Motivation
• With exponential growth of load demand, the present electricity grid is showing
signs of obsolescence and inadequacy.
• In order to meet the rising energy demand as well as the increasing quality of
service requirements, the existing power grid infrastructure is slowly evolving into
a smart grid.
• Smart Grid is a system of information and communication applications integrated
with electric generation, transmission, distribution, and end use technologies that:
a. Promote Customer Choice - enables consumers to mange and choose the most
economically efficient offerings
b. Improve Reliability - Uses automation and alternative resources to maintain
delivery system reliability and stability
c. Integrate Renewable - integrates renewable, storage and generation alternatives.
• Dynamic Pricing mechanism helps improve effectiveness and reliability of a
smart grid by facilitating Demand Response. This motivates the development of
Real Time Pricing Simulator in a Smart Grid environment.
Index
4. 4
44
Challenges
• Traditionally, electricity consumption for
residential consumers has been recorded through
bulk usage meters over a given period (typically 30
days)
• With the advent of smart meter technology,
utilities can record this consumption as often as
every 15 minutes
• Smart meters enable consumers monitor their load
pattern and schedule it optimally for cost savings
and reduced strain on the grid
• Despite the promise of substantial economic gains,
the deployment of dynamic pricing for residential
consumers has been remarkably tepid
• Today 5 % customers are on Advanced Metering
Infrastructure (AMI) but less than one-tenth of that
number are estimated to be on dynamic pricing
Index
5. 5
55
Challenges
• It is believed that dynamic pricing inflicts harm on low-income
consumers, seniors, people with disabilities, people with young
children, and small businesses
• These consumers are unable to curtail peak period usage because they
have very little connected load to begin with
• Therefore, the greatest barriers in implementation of dynamic pricing
are legislative and regulatory, deriving from state efforts to protect
retail customers from the vagaries of competitive markets
• The project caters to this barrier in implementation of dynamic
pricing by showing substantial monetary gains for residential
consumers (which constitute the families, low income consumers etc.)
by adopting the Real Time Pricing mechanism for calculation of
electricity charges.
Index
7. 7
77
Dynamic Pricing-Types
RTP prices vary on an hourly basis and the
customer is charged a different price for each
interval
RTP prices vary on an hourly basis and the
customer is charged a different price for each
interval
TOU breaks up the day into broad blocks of hours
(Peak, off- and interim)where the price for each
period is predetermined and constant
TOU breaks up the day into broad blocks of hours
(Peak, off- and interim)where the price for each
period is predetermined and constant
VPP is a hybrid of TOU and RTP .Different periods
for pricing are defined in advance but the price for
peak period varies by utility & market conditions.
VPP is a hybrid of TOU and RTP .Different periods
for pricing are defined in advance but the price for
peak period varies by utility & market conditions.
CPP pricing customers face a high price for peak
time electricity use on certain days of the year,
generally identified as “critical events”
CPP pricing customers face a high price for peak
time electricity use on certain days of the year,
generally identified as “critical events”
CPR -the utility pays customer for each kWh of
electricity reduced during the peak hours of
critical event days relative to baseline amounts
CPR -the utility pays customer for each kWh of
electricity reduced during the peak hours of
critical event days relative to baseline amounts
ENERGY DEMAND
Off Peak Hours : 11pm-7am
Interim Hours: 7am-1pm
Peak Hours: 2pm-8pm
Index
8. 8
88
Why Real Time Pricing (RTP) ?
• Purest form of dynamic pricing and
ideal from a price signal perspective
• Highest financial rewards in
comparison to other dynamic pricing
mechanisms
• Customers assume the risk of
wholesale price volatility and are
rewarded with less cost of service.
• Customers pay electricity prices that
are linked to the wholesale cost of
electricity on an hourly (or sub-hourly)
basis.
Index
9. 9
99
Real Time Pricing in Smart Grid
“Smart Rates” are essential to realise the benefits of Smart Grid
RTP:
• Encourages conservation and shifting of electricity consumption to times when
electricity is cheaper
• Motivates utilization of renewable resources like PV systems during high-priced
peak times when centralized power supply is constrained and/or transmission
and distribution systems are congested
• Improves the financial attractiveness of Distributed Energy Resources (DES).
For example, if for rooftop solar the peak period occurs during times of
abundant solar generation, it can result in significant cost savings
• Stimulates investment in energy-efficient appliances, helping customers
conserve during high-priced times
Index
10. 10
1010
Literature Review
• The existing research in real time pricing can be divided into four
categories[6]:
a. Related to how users respond to real time price to achieve their
desired level of comfort with lower electricity bill payment
b. Related to setting the real-time electricity price at the retailer side,
without taking into account users’ potential responses to the
forecasted price.
c. Related to setting the real-time retail electricity price based on the
maximization of the aggregate surplus of users and retailers subject
to the supply-demand matching
d. Theoretical and simulation studies focused on understanding the
economic advantages of RTP
Index
11. 11
1111
Literature Review
Category a)
• Mohesian-Rad et al proposed an optimal and automatic residential
energy consumption scheduling framework based on linear programming
and weighted average price prediction filter in presence of a real-time
pricing tariff [7].
• Mohesian-Rad et al considers a power network where end customers
choose their daily schedules of their household appliances/loads by
playing games among themselves and the utility company tries to adopt
adequate pricing tariffs that differentiate the energy usage in time and
level to make the Nash equilibrium minimize the energy costs[8].
Category b)
• Borenstein et al discussed various factors that determine the setting of
real-time price at the retailer side[9].
Index
12. 12
1212
Literature Review
Category c)
• Na Li et al proposed a distributed algorithm for the utility company and the
customers to jointly compute optimal real time prices and demand
schedules that would maximise the pay-offs of both[10].
• Lijun Chen et al proposed distributed demand response algorithms to
match power supply and demand in competitive as well as oligopolistic
markets[11].
• Roozbehani et al proposed a mathematical model for the dynamic
evolution of supply, demand, and clearing price where adjusted load
demand by consumers is given as feedback signal to the wholesale market
which affects the prices for next time step[12].
Category d)
• P. Centolella[13], S. Borenstein[9] and B. Alexander[14] have discussed the
economic benefits of real time pricing for people belonging to different
income groups.
Index
13. 13
1313
Big Idea and Objective
• To determine the potential monetary savings by adopting real time
electricity pricing mechanism in an smart grid environment as
compared to flat rate pricing mechanism in vogue.
• To determine the effectiveness of real time pricing as a Demand
Response strategy , an essential feature of smart grid infrastructure.
• The approach adopted is as follows:
Index
14. 14
1414
1. Data Collection
• Load Profile data for Residential Loads in Illinois was downloaded
from Open Energy Information (OpenEI), a U.S. Department of
Energy website.
(URL: http://en.openei.org/datasets/files/961/pub/)
• Hourly Real Time Electricity tariff of Commonwealth Edison
(ComEd), electric utility in Illinois, was used for simulation.
(URL: https://hourlypricing.comed.com/live-prices/month/)
• Hourly Temperature and Solar Irradiation data for Illinois was
obtained from System Advisor Model(SAM), a performance and
financial model designed by NREL for PV Systems.
(URL: https://sam.nrel.gov/)
Index
15. 15
1515
2. Modelling of Smart Grid in Simulink
• This model of smart grid is based on the one developed by Centre for
Electromechanics, University of Texas (Austin) for Pecan Street Inc. with some
modifications.
Index
16. 16
1616
2. Modelling of Smart Grid in Simulink
RESIDENTIAL LOAD SUBSYSTEM
• The load profile and solar generation for each
of the 5 houses is read from a MATLAB file using
From File block in SIMULINK
•The Pgrid is determined for five scenarios:
•House 1- No PV generation. Peak Demand
occurs during peak price period. Load demand
and RTP data for June’15 used.
•House 2- No PV Generation. Load demand peak
shifted to off peak period (Demand Response).
•House 3 - No PV generation. Load demand and
RTP data for November’15 used.
•House 4- PV system installed. Power drawn from
grid becomes less.
•House 5- PV system installed. Also, load demand
peak shifted to off peak period.
Index
17. 17
1717
2. Modelling of Smart Grid in Simulink
MODEL OF PV SYSTEM
INSTALLED ON HOUSES 4&5
SPECIFICATIONS OF PV ARRAY
USED FOR SIMULATION
Index
18. 18
1818
3.Analysis of Simulation Results
• The following three curves are obtained upon simulating the
Smart Grid model:
• Load Demand Curve (Pload)
• Power Output Curve (Psolar)
• Power drawn from the grid(Pgrid)
This power
consumption value is
used to calculate the
electricity charges
incurred by the user.
This power
consumption value is
used to calculate the
electricity charges
incurred by the user.
This is the power
generated by PV
array for given
temperature and
irradiance.
This is the power
generated by PV
array for given
temperature and
irradiance.
Index
19. 19
1919
4. Calculation of Electricity Charges
• The algorithm for calculation of electricity charges was coded in
MATLAB.
• Sample electricity bills on the website of ComEd were referred to
develop the algorithm and to determine different rates including:
a. Electricity Supply Charge
b. Transmission Services Charge
c. Customer Charge
d. Standard Metering Charge
e. Distribution Facilities Charge
f. IL Electricity Distribution Charge
g. Environmental Cost Recovery Adjustment
h. Energy Efficiency Program Costs
i. Franchise Cost
j. Sales Tax
Index
20. 20
2020
4. Calculation of Electricity Charges
• Electricity Supply Charge (ESC) for Flat Rate Mechanism
• Electricity Supply Charge (ESC) for RTP Mechanism
• Only the methodology for ESC charge calculation varies for the
two pricing mechanisms. The other cost components of the bill
remain same.
ESC = Total KWh drawn from grid * Flat Rate($/KWh)ESC = Total KWh drawn from grid * Flat Rate($/KWh)
ESC = Hourly KWh drawn from grid * HourlyƩ
RTP($/KWh)
ESC = Hourly KWh drawn from grid * HourlyƩ
RTP($/KWh)
Index
21. 21
2121
4. Calculation of Electricity Charges
Calculation of total kWh drawn from grid (considering PV system installed)
Smart Grid model developed in SIMULINK is simulated
Hourly PV output power data is exported from the
simulation (using the To File block) to MATLAB
Equations are derived for the load demand curve(A) and PV
output power curve(B) using the Curve Fitting App
Area under both the curves is found in MATLAB
(using integral command)
Area under curve B is subtracted from Area under curve A
to calculate the total kWh drawn from the grid
Index
22. 22
2222
Simulation Implementation
• The project was executed using
• The following features of MATLAB were used for modeling:
a. SIMULINK- Modeling of the smart grid was done using blocks from
the following libraries: SimPowerSystems and Simulink.
b. Curve Fitting APP- This Matlab APP was used to derive the equation
for the power output curve of PV Array and the load demand curve.
c. Graphical User Interface(GUI)- GUI was used to display the
electricity bill calculations post the simulation based on both RTP and
Flat Rate mechanisms to facilitate comparison.
d. Function Handles – These were used to compute total kWh
consumed to determine the electricity charges and for
implementation of the GUI.
Index
24. 24
2424
Simulation Results
• This GUI appears upon
execution of the MATLAB
code
• It displays the calculated
daily electricity charges (in
dollars) for the five houses
under different scenarios
• As visible, the real time
charges are lower than flat
rate charges
• The smart grid model can be
accessed by clicking Browse
Index
28. 28
2828
Discussion - Advantages
• Flexibility of the Smart Grid Model – The parameters and
specifications for different blocks in the model can be modified
as per requirement to evaluate the net power drawn from the
grid by the load.
• Algorithm for Electricity Charges - For this project, cost savings
have been calculated for the state of Illinois. This analysis can
be extended to different U.S. states by modifying the tariff
rates to enable users to compare savings from RTP with the
present tariff system.
• Use of Realistic Data – The model uses realistic and easily
available data for simulation. This helps to obtain the
simulation results with high accuracy.
Index
29. 29
2929
Discussion - Advantages
• Visualization of Demand Response – The model can be used to
visualize and evaluate the benefits of demand response and its
effectiveness in making the present grid more efficient and
reliable.
Index
30. 30
3030
Limitations and Possibilities
• Simplicity of the Residential Load - The
model assumes residential load comprising
of two 120V and one 240V load. A more
detailed and realistic load needs to be
modeled to understand the magnitude of
cost savings.
• Modelling of PV System - In this model, the
PV system is not designed to operate at
maximum power point at all times. MPPT
algorithm can incorporated in the model to
enhance the power output from the PV
system and increase cost savings.
Index
31. 31
3131
Limitations and Possibilities
• Integration of Renewable Resources other
than PV – Power generation from only PV
systems has been considered. The model
can be made more realistic by
incorporating power generation other
sources such as wind, batteries etc.
• Modelling of Dynamic Loads – Only
residential loads have been modelled in
the smart grid. The analysis can also be
extended to industrial loads.
Index
32. 32
3232
Conclusions
• Real Time Pricing (RTP) reflects the time and location specific
variations in the cost of producing and delivering electricity and
therefore, is a more effective pricing mechanism than Flat Rate
Pricing.
• RTP results in significant monetary savings due to reduction in the
electricity bills .
• RTP engages the consumer directly in peak load reduction thereby
reducing the strain on the grid.
• RTP facilitates integration of renewable resources with the grid.
Therefore, RTP is essential to realise the benefits of a smart grid.
Index
33. 33
3333
References
1. Faruqui, A. (2012). The Ethics of Dynamic Pricing. Smart Grid, 23(6), 61–83.
http://doi.org/10.1016/B978-0-12-386452-9.00003-6
2. Roycroft, T. (2010). The Impact of Dynamic Pricing on Low Income Consumers: Evaluation of the IEE
Low Income Whitepaper, (September).
3. Webinar, N. (2010). Dynamic Pricing in a Smart Grid World Webinar Objectives. Group, 1–50.
4. Center for Electromechanics , University of Texas (Austin)
5. Badtke-berkow, M. (n.d.). A Primer on Electricity Pricing Authors.
6. Qian, L. P., Zhang, Y. J. A., Huang, J., & Wu, Y. (2013). Demand response management via real-time
electricity price control in smart grids. IEEE Journal on Selected Areas in Communications, 31(7),
1268–1280. http://doi.org/10.1109/JSAC.2013.130710
7. Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal residential load control with price
prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 1(2), 120–
133. http://doi.org/10.1109/TSG.2010.2055903
8. H. Mohsenian-Rad, W.S. Wong, J. Jatskevich, R. Schober, and A.Leon-Garcia, “Autonomous demand
dide management based on gametheoretic energy consumption scheduling for the future Smart
Grid,”IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320-331, Dec.2010.
9. S. Borenstein, M. Jaske, and A. Rosenfeld, “Dynamic pricing, advanced metering, and demand
response in electricity markets,” UC Berkeley: Center for the Study of Energy Markets.
Index
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3434
References
10. N. Li, L. Chen, and S. H. Low, “Optimal demand response based on utility maximization in power
networks,” Proc. IEEE power engineering society general meeting, pp. 1-8, Jul. 2011.
11. L. Chen, W. Li, S. H. Low, and K. Wang, “Two Market Models for Demand Response in Power
Networks,” Proc. IEEE SmartGridComm, pp. 397-402, Oct. 2010.
12. M. Roozbehani, M. Dahleh, and S. Mitter “On the stability of wholesale electricity Markets under
Real-Time Pricing,” Proc. IEEE Conference on Decision and Control, pp. 1911-1918, Dec. 2010.
13. P. Centolella, “The integration of price responsive demand into regional transmission organization
(RTO) wholesale power markets and system operations,” Energy, to be published.
14. B. Alexander, Smart meters, real time pricing, and demand response programs: Implications for low
income electric customers Oak Ridge Natl. Lab., Tech. Rep., Feb. 2007.
15. www.mathworks.com
Index
