Study of real-time electricity pricing pattern in NZ

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Study of real-time electricity pricing pattern in NZ

Suresh Palapati - Presenter,

Department of Computing, Electrical and Applied Technology

Unitec Institute of Technology Research team

Suresh Palapati and Niranjan Singh

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Study of real-time electricity pricing pattern in NZ

Project outline

• Introduction

• Motivation

• Rationale

• Literature review

• Methodology

• Results

• Conclusion and future

• References

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Study of real-time electricity pricing pattern in NZ

Introduction

Several features of electricity markets make them quite different from most other markets.

1. Electricity cannot be stored (in large quantities) 2. Supply must equal demand instantaneously (too

much or too little supply may lead to rolling blackouts or even system collapse

^).

3. Many economists have argued that electricity markets

would work better if customers were charged the real-

time electricity price.

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Real-time pricing

1. Real-time pricing, also known as dynamic pricing, is a utility rate structure in which the per-kWh charge varies each hour based on the utility's real- time production costs.

2. The cost to generate electricity is different for each power plant, i.e. the

cost to generate each kilowatt-hour (KWh) of electricity changes constantly depending on which plants are operating and how cost-effective those

plants are.

3. The limitations of the traditional electric meter made it impossible to track how much energy (KWh) was purchased and when, so the energy was priced based on averages.

4. Everyone paid the average price per kWh regardless of when that KWh was consumed.

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Study of real-time electricity pricing pattern in NZ

Flat Rate v/s RTP/Dynamic

Pricing

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Why real-time pricing?

1. Encourages conservation and shifting of electricity consumption to times when electricity is cheaper.

2. Motivates using renewable resources like Photo voltaic systems during high-priced peak times when the centralized power supply is constrained and/or transmission, and distribution systems are congested.

3. Real-time pricing will result in more consumer participation and increased competition in the wholesale, hedge and retail markets.

4. Generators will be more responsive and accurate with their pricing in response to real-time pricing capabilities.

5. Encourage the use of energy-efficient appliances, helping customers conserve during high-priced times.

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Study of real-time electricity pricing pattern in NZ

Real-time pricing in New Zealand

1. Real-time pricing in New Zealand is still in the evolution phase.

2. Real-time pricing is only available for wholesale customers.

3. Only one electricity company is passing on RTP to its customers.

4. Determining and understanding the pattern of the RTP in NZ.

5. To determine the potential monetary savings by adopting real-time 6. Electricity pricing mechanism in a smart grid environment as

compared to a flat rate pricing mechanism.

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Impact of real-time pricing in New Zealand

1. Encourages conservation and shifting of electricity consumption to times when electricity is cheaper

2. Motivates utilisation of renewable resources like PV systems during high-priced peak times when a centralized power supply is

constrained and/or transmission, and distribution systems are

congested

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Literature overview

The existing research in real-time pricing can be divided into three categories.

1. Related to how users respond to real-time price to achieve their desired comfort level with lower electricity bill payment.

2. Related to setting the real-time electricity price at the retailer side, without considering users’ potential responses to the forecasted price.

3. Theoretical and simulation studies focused on understanding the economic advantages of real-time pricing

.

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Study of real-time electricity pricing pattern in NZ

Literature review

1. Category 1:Mohesian-Rad et al. proposed an optimal and automatic residential energy consumption scheduling framework that attempts to achieve the desired trade-off between minimizing the electricity payment and minimizing the waiting time for each household appliance's operation in the presence of a real-time pricing tariff.

2. Category 2:Borenstein et al. discussed various factors that determine the setting of real-time prices on the retailer side.

3. Category 3: 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 both pay-offs.

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Study of real-time electricity pricing pattern in NZ

Methodology

Data collection and manipulation

1. Real time pricing data is sourced from the Electricity authority website.

(URL:www.emi.ea.govt.nz ).

2. The data is zipped and stored as EMI data sets.

3. The data is not formatted and is stored as it is received from the wholesale information and trading system(WITS).

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Study of real-time electricity pricing pattern in NZ

Data collection and sorting

1. The Data have RTP prices every 5 minutes from 240 Grid exit points(Grids).

2. Each RTP price file contains data about a single 5-minute interval.

3. There are six 5-minute intervals per half-hour trading period, giving rise to 288 RTP files per day, all collected into the daily zip file.

4. Each day will have around 70 thousand lines of data with RTP pricing information every 5 minutes and from each grid point.

5. The data is from 60 days from the Months of January and July, covering the Summer and winter months.

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Study of real-time electricity pricing pattern in NZ

Data analysis using Tableau

• The data is manipulated and refined into one single data file for 60

• The data file for 60 days contains 4216320 rows of data. days

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Data collection and sorting

The sorted weather data will have

1. Grid from which the electricity is sourced, there are 242 grid or grid exit points 2. Price/MWH price per Mega watthour,

3. Island (North Island –NI South Island –SI), 4. Region ( as per data 10 regions )

5. Date and Time: per every 5 minutes

AK-Auckland PN-Palmerston North

BP-Bay of plenty, WN-Wellington

HM-Hamilton CH-Christchurch

NL-North land IN-Invercargill

NR-Nelson region WC- west coast

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Study of real-time electricity pricing pattern in NZ

Analysis of data: Grid (GXP) in New Zealand

Island Region Grid

North Island(NI) Auckland (AK) 24

North Island(NI) Bay of Plenty (BP) 27

North Island(NI) Hamilton (HM) 39

North Island(NI) North Land (NL) 11

North Island(NI) Nelson Region (NR) 14

North Island(NI) Palmerston North (PN) 28

North Island(NI) Wellington (WN) 17

Total NI 160

South Island (SI) Christchurch (CH) 20

South Island (SI) Invercargill (IN) 35

South Island (SI) West Coast (WC) 27

Total SI 82

Total NZ 242

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Study of real-time electricity pricing pattern in NZ

Analysis of data: Grid (GXP) in New Zealand

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Analysis of data: RTP price North and South Island

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Analysis of data: RTP price per region

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Study of real-time electricity pricing pattern in NZ

Analysis of data: RTP price Pattern

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Analysis of data: RTP price pattern (All Grids)

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Conclusion

1. South island’s average RTP price is cheaper compared to North Island.

2. Northland’s average RTP price is highest at $64.63, whereas Invercargill is cheapest at $52.23.

3. RT price is always cheaper during off-peak and expensive during high- demand times.

4. RTP pattern follows the same trend for the North and South Island.

5. This will help plan the power usage as per the pattern.

6. Maximise the returns from using renewable power systems, i.e. Solar

and wind.

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Benefits and challenges

1. This will help plan the power usage making the cost of power cheaper.

2. Maximise the returns from using renewable power systems, i.e. Solar and wind.

3. Investing in a Hybrid grid is more appealing.

4. End up paying more if there is no planning.

5. Still not available in NZ.

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Future work

1. Larger data samples can be used to further analyse to have a more accurate pattern.

2. Further study can be done to have a smart grid, which can plan the usage according to the price pattern.

3. Smarter integration of wind farms and solar systems.

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References

Electricity authority webiste. www.emi.ea.govt.nz

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,

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.

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.

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Study of real-time electricity pricing pattern in NZ

References

Pébereau, C., & Remmy, K. (2022). Barriers to real-time electricity pricing:

Evidence from New Zealand (No. crctr224_2022_339). The University of Bonn and the University of Mannheim, Germany.

Hongbo Zhu, Yan Gao, Yong Hou, "Real-Time Pricing for Demand Response in Smart Grid Based on Alternating Direction Method of Multipliers", Mathematical Problems in Engineering, vol. 2018, Article ID 8760575, 10 pages, 2018.

https://doi.org/10.1155/2018/8760575

Badtke-berkow, M. (n.d.). A Primer on Electricity Pricing Authors.