research-article

Determining Prosumer Energy Generation Performance as Basis for Peer-to-Peer Energy Trading Decisions using Monte Carlo Simulation

Authors:

Ralph Voltaire Voltaire Dayot,

Hyuntae Kim,

In-Ho RaAuthors Info & Claims

SMA 2020: The 9th International Conference on Smart Media and Applications

Pages 258 - 262

https://doi.org/10.1145/3426020.3426086

Published: 04 November 2021 Publication History

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Abstract

The increased penetration of renewable energy resources has become one of the driving forces with a rapid growth of the number of prosumers in the energy market. The aim of maximizing prosumer revenue and welfare has been one of the motivations due to a growing interest in the field of Transactive Energy (TE). This paper presents a novel method for determining and forecasting a prosumer's energy generation performance by using Monte Carlo Simulation (MCS) with Geometric Brownian Motion (GBM). With the implementation of GBM, the average generation of a prosumer is forecasted based on multiple timesteps to visualize multiple outcomes. In addition, the computation of potential payoffs/revenues to be received by the prosumers based on average energy generation and randomized prices has been carried out. Finally, the experimental results of this work provide beneficial insights into future decisions concerning the improvement of prosumer performance.

References

[1]

S. Razzaq, R. Zafar, N. A. Khan, A. R. Butt, and A. Mahmood, “A Novel Prosumer-Based Energy Sharing And Management (PESM) Approach For Cooperative Demand Side Management (DSM) In Smart Grid,” Applied Science, vol. 6, no. 10, 2016.

Crossref

Google Scholar

[2]

A. Paudel, K. Chaudhari, C. Long, and H. B. Gooi, “Peer-To-Peer Energy Trading In A Prosumer-Based Community Microgrid: A Game-Theoretic Model,” IEEE Transactions Industry Electronics, vol. 66, no. 8, pp. 6087–6097, 2019.

Crossref

Google Scholar

[3]

O. Abrishambaf, F. Lezama, P. Faria, and Z. Vale, “Towards Transactive Energy Systems: An Analysis on Current Trends,” Energy Strategy Reviews, vol. 26, p. 100418, 2019.

Crossref

Google Scholar

[4]

S. Yin, J. Wang, and F. Qiu, “Decentralized Electricity Market with Transactive Energy – A Path Forward,” Electricity Journal, vol. 32, no. 4, pp. 7–13, 2019.

Crossref

Google Scholar

[5]

F. Pallonetto, M. De Rosa, F. Milano, and D. P. Finn, “Demand Response Algorithms for Smart-Grid Ready Residential Buildings Using Machine Learning Models,” Applied Energy, vol. 239, no. February, pp. 1265–1282, 2019.

Crossref

Google Scholar

[6]

C. Essayeh, M. Raiss El-Fenni, and H. Dahmouni, “Cost-Effective Energy Usage in a Microgrid Using a Learning Algorithm,” Wireless Communications and Mobile Computing, 2018.

Crossref

Google Scholar

[7]

M. Lotfi, C. Monteiro, M. S. Javadi, M. Shafie-Khah, and J. P. S. Catalao, “Optimal Prosumer Scheduling in Transactive Energy Networks Based on Energy Value Signals,” In Proceeding of the 2nd International Conference on Smart Energy Systems and Technologies (SEST 2019), 2019.

Crossref

Google Scholar

[8]

“Python Risk Management: Monte Carlo Simulations | by Lester Leong | Towards Data Science.” [Online]. Available: https://towardsdatascience.com/python-risk-management-monte-carlo-simulations-7d41c891cb5. [Accessed: 10-Aug-2020].

Google Scholar

[9]

D. L. Abdelsamad Ahmed, "Reliability Analysis for a Hybrid Microgrid based on Chronological Monte Carlo Simulation with Markov Switching Modeling," In Proceedings of the IEEE International Conference on Probabilistic Methods Applied to Power Systems, pp. 3–7, 2018. 10.1109/ISGT.2019.8791611

Crossref

Google Scholar

[10]

R. Hanna, V. R. Disfani, and J. Kleissl, "Reliability Evaluation for Microgrids using Cross-Entropy Monte Carlo simulation," In Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems PMAPS 2018, pp. 1–6, 2018.

Crossref

Google Scholar

[11]

M. Wadi, M. Baysal, A. Shobole, and M. R. Tur, “Reliability Evaluation in Smart Grids via Modified Monte Carlo Simulation Method,”. In Proceedings of the 7th International IEEE Conference on Renewable Energy Research Applications ICRERA 2018, December, pp. 841–845, 2018.

Crossref

Google Scholar

[12]

J. Liang, Y. Miao, X. Li, Z. Wang, and L. Yin, “Electric Heating Load Forecasting Model and Method Based on Monte Carlo Method,” In Proceedings of the IEEE Sustain. Power Energy Conference on Grid Modernization Energy Revolution (iSPEC 2019)., pp. 2403–2406, 2019.

Crossref

Google Scholar

[13]

D. M. K. Deborah A. Sunter, Bryan Murray, Marcus Lehmann, Rachael Green, Bryant Ke, Brooke Maushund, “Two-stage Monte Carlo Simulation to Forecast Levelized Cost of Electricity for Wave Energy,”. In Proceeding of IEEE 6th International Conference on Renewable Energy Research and Applications, vol. 5, pp. 5–8, 2017.

Crossref

Google Scholar

[14]

M. M. P. Chowdhury, T. C. Son, C. Kiekintveld, and W. Yeoh, “Bidding Strategy for Periodic Double Auctions Using Monte Carlo Tree Search,” In Proceeding of the International Joint. Conference on Autonomous Agents and Multiagent Systems AAMAS, vol. 3, pp. 1897–1899, 2018.

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Saghiri AMoazeni S(2023)A Generic Parametric Framework for Peer-to-Peer Electricity Market Design2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)10.1109/EEEIC/ICPSEurope57605.2023.10194773(1-6)Online publication date: 6-Jun-2023
https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194773

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SMA 2020: The 9th International Conference on Smart Media and Applications

September 2020

491 pages

ISBN:9781450389259

DOI:10.1145/3426020

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 November 2021

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SMA 2020: The 9th International Conference on Smart Media and Applications

September 17 - 19, 2020

Jeju, Republic of Korea

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Saghiri AMoazeni S(2023)A Generic Parametric Framework for Peer-to-Peer Electricity Market Design2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)10.1109/EEEIC/ICPSEurope57605.2023.10194773(1-6)Online publication date: 6-Jun-2023
https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194773

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Robust optimization and pricing of Peer-to-Peer energy trading considering battery storage

Peer to Peer Distributed Solar Energy Trading

Efficient Peer-to-Peer Energy Trading Mechanisms with Unreliable Prosumers

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