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Peer-to-Peer Energy Trading: A Case Study Considering Network Constraints

Jaysson GuerreroArchie ChapmanGregor Verbič

School of Electrical and Information Engineering

Asia-Pacific Solar Research Conference

(4th December 2018)

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Introduction and Motivation

– Future of Electrical Power Systems: Renewable electricitygeneration and energy storage technology.

– Local generation and storage are used to meet self-demandand the energy surplus may be exported to the grid.

Consumers Prosumers

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Introduction and Motivation

– All stakeholders will have to respond to new scenarios. Ingeneral, the next concepts are some insights of the future vision:

New Services

Business Model

Evolution

Regulatory Framework

Centralised, Distributed or Decentralised

Include new services and support the efficient uptake and use of distributed energy resources (DERs).

New roles, market platforms and price signals.

Changes on the existing regulatory framework for new business models to support the coming scenarios.

What is the best structure for the future scenarios?

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Introduction and Motivation

– All stakeholders will have to respond to new scenarios. Ingeneral, the next concepts are some insights of the future vision:

New Services

Business Model

Evolution

Regulatory Framework

Centralised, Distributed or Decentralised

Include new services and support the efficient uptake and use of distributed energy resources (DERs).

New roles, market platforms and price signals.

Changes on the existing regulatory framework for new business models to support the coming scenarios.

What is the best structure for the future scenarios?Local Energy Trading on Low-Voltage Networks

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Introduction and Motivation

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Introduction and Motivation

P2PDLTs

(blockchain)

Cryptocurrencies

Network constraints

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P2P Energy Trading - MethodologyProblem Formulation

Customers

Network Constraints

Market Mechanism

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P2P Energy Trading - Methodology1. Customers Model

– We consider a smart grid system for energy trading at locallevel.

PV system. Battery storage. Home energy management

system (HEMS)

Consumers Prosumers

Customers

Surplus

Maximise the benefits of PV-storage systems.Schedule and coordinate energy use

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P2P Energy Trading - Methodology2. Network

Voltage sensitivity coefficients (VSCs)

Power transfer distribution factors (PTDFs)

Loss sensitivity factors (LSFs)

Voltage variations as function of the power injections in the network.

Reflect the change in active power line flows due to an exchange of active

power flow between two nodes.

Reflect the portion of system losses due to power injections or absorptions in the

network.

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P2P Energy Trading - Methodology3. Market Mechanism

Local Market

Price, Quantity Price, Quantity

a) Continuous Double Auction (CDA).b) Self-interested agents - Bidding strategies. c) Network Permission Structure.

(𝑝𝑝𝑠𝑠, 𝑞𝑞𝑠𝑠) (𝑝𝑝𝑏𝑏, 𝑞𝑞𝑏𝑏)

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Methodology3.a. Continuous Double Auction - CDA

– The operation of the market in this case involves only twoparties interested in the trading (buyers and sellers). In order toachieve an individual welfare, agents submit asks/bids basedon their preferences and costs.

Buyers bids Sellers asksBuyer Volume Price Time Seller Volume Price Time

b5 200 35.21 8:15am s8 150 35.31 8:20am

b1 300 35.19 8:10am s2 200 35.35 8:12am

b2 785 35.15 8:21am s6 1000 35.55 8:00am

b9 170 35.11 8:05am s7 300 35.56 8:15am

… …

… …

Buyers bids Sellers asks

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Buyers bids Sellers asks

CDA – Example

Buyers bids Sellers asksBuyer Volume Price Time Seller Volume Price Time

b5 200 35.21 8:15am s10 100 35.21 8:22am

b1 300 35.19 8:10am s8 150 35.31 8:20am

b2 785 35.15 8:21am s2 200 35.35 8:12am

b9 170 35.11 8:05am s6 1000 35.55 8:00am

… s7 300 35.56 8:15am

… …

𝑡𝑡 = 1

𝑡𝑡 = 2New ask

Buyers bids Sellers asksBuyer Volume Price Time Seller Volume Price Time

b5 200 35.21 8:15am s8 150 35.31 8:20am

b1 300 35.19 8:10am s2 200 35.35 8:12am

b2 785 35.15 8:21am s6 1000 35.55 8:00am

b9 170 35.11 8:05am s7 300 35.56 8:15am

… …

… …

Buyers bids Sellers asks

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CDA - Example

Buyers bids Sellers asksBuyer Volume Price Time Seller Volume Price Time

b5 100 35.21 8:15am s8 150 35.31 8:20am

b1 300 35.19 8:10am s2 200 35.35 8:12am

b2 785 35.15 8:21am s6 1000 35.55 8:00am

b9 170 35.11 8:05am s7 300 35.56 8:15am

… …

… …

𝑡𝑡 = 3 Buyers bids Sellers asks

– Zero intelligence plus (ZIP) traders can trade very effectively ina simulated market [1].

– ZIP traders adapt and update profit margins based on thematching of previous orders [2].

3.b. Agents

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Methodology3.c. Network Permission Structure

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Implementation Case study

– Scenario 1: High PV Penetration (Impact Probabilistic Analysis [3])– Scenario 2: P2P energy trading.

– The model of users is based on CREST model [4], which is a high-resolution stochastic model of electricity demand. This modelsimulates electrical demand and generation due to appliances,lighting and photovoltaics systems.

PV systems - 7 kWpBattery – 10 kWh

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Results – Scenario 1 - High PV Penetration• Voltage profiles throughout the day.

• Percentage of users with overvoltage issues at different levels of PV penetration in the network

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Results – Scenario 2 – P2P Energy Trading

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• Voltage levels at households’ nodes.

Results – Scenario 2 – P2P Energy Trading

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Conclusions & Future Work

– We have proposed a local market in which consumers andprosumers can trade energy. We explicitly considered theimpact of the injection and absorption of power in the networkin a P2P exchange.

– Our results show the benefits that the local market will bring totheir participants.

– The benefits could be improved with better trading strategies.

– For future work, our interest is to extend the study of agent’sstrategies with a more extensive analysis including flexibleloads.

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References

1) D. K. Gode and S. Sunder, “Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality,”Journal of Political Economy. 1993.

2) D. Cliff and J. Bruten, “Minimal-intelligence agents for bargaining behaviorsin market-based environments,” In HPL-97-91, HP Laboratories Bristol, Aug.1997.

3) A. Navarro-Espinosa and L. F. Ochoa, “Probabilistic impact assessment oflow carbon technologies in LV distribution systems,” IEEE Trans. Power Syst.,vol. 31, no. 3, pp. 2192–2203, May 2016.

4) E. McKenna and M. Thomson. 2016. High-resolution stochastic integratedthermal-electrical domestic demand model. Applied Energy, 165-445.

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Thank you !Any Questions?

Jaysson Guerrero PhD Candidate

Email: jaysson.guerrero@sydney.edu.au

School of Electrical and Information Engineering

The University of SydneyRoom 329, Electrical Engineering Building, J03The University of Sydney | NSW | 2006 | Australia