Real Time Pricing Simulator for a Smart Grid

Swantika Dhundia

Power System Operation and Control

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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)

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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.

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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

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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.

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Electricity Pricing

Environmental Benefits

Flat Rate v/s Dynamic Pricing

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Dynamic Pricing-Types

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

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”

CPR -the utility pays customer for each kWh of electricity reduced during the peak hours of critical event days relative to baseline amounts

ENERGY DEMANDOff Peak Hours : 11pm-7amInterim Hours: 7am-1pmPeak Hours: 2pm-8pm

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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.

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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

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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 paymentb. Related to setting the real-time electricity price at the retailer side,

without taking into account users’ potential responses to theforecasted 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 theeconomic advantages of RTP

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Literature ReviewCategory 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].

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Literature ReviewCategory 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.

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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:

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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/)

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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.

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2. Modelling of Smart Grid in SimulinkRESIDENTIAL 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.

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2. Modelling of Smart Grid in SimulinkMODEL OF PV SYSTEM

INSTALLED ON HOUSES 4&5SPECIFICATIONS OF PV ARRAY

USED FOR SIMULATION

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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 is the power generated by PV array for given

temperature and irradiance.

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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 Chargeb. Transmission Services Chargec. Customer Charged. Standard Metering Chargee. Distribution Facilities Chargef. IL Electricity Distribution Chargeg. Environmental Cost Recovery Adjustmenth. Energy Efficiency Program Costsi. Franchise Costj. Sales Tax

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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 = Ʃ Hourly KWh drawn from grid * Hourly RTP($/KWh)

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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

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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.

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MATLAB CODE

Foxit PDF Document

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•The presentation should be in the Normal Mode (not in the slide show mode) to open the pdf doc

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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

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Simulation Results

House 1

House 2

0 5 10 15 20 25Time (in hours)

0

1

2

3

4

5

6

7

8

Load Demand (kW)Electricity price (cents/kWh)

0 5 10 15 20 25Time (in hours)

0

1

2

3

4

5

6

7

8

Load Demand (kW)Electricity Price (cents/kWh)

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Simulation Results

House 3

House 4

0 5 10 15 20 25Time (in hours)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Load Demand (kW)

Electricity Price (cents/kWh)

0 5 10 15 20 25Time (in hours)

-2

-1

0

1

2

3

4

5

6

Load Demand (kW)

Solar Panel Output (kW)

Net Power drawn from grid (kW)

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Simulation Results

House 5

0 5 10 15 20 25Time (in hours)

-2

-1

0

1

2

3

4

5

Load Demand (kW)

Power Output from PV (kW)

Power drawn from Grid (kW)

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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.

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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.

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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.

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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.

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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.

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References1. Faruqui, A. (2012). The Ethics of Dynamic Pricing. Smart Grid, 23(6), 61–83. http://doi.org/10.1016/B978-0-12-386452-

9.00003-62. 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.2013.1307107. 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.2010.20559038. 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.

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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

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