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Journal articles on the topic 'Peer-to-peer energy trading'

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Author: Grafiati

Published: 10 December 2022

Last updated: 20 February 2023

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1

Wongthongtham, Pornpit, Daniel Marrable, Bilal Abu-Salih, Xin Liu, and Greg Morrison. "Blockchain-enabled Peer-to-Peer energy trading." Computers & Electrical Engineering 94 (September 2021): 107299. http://dx.doi.org/10.1016/j.compeleceng.2021.107299.

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2

Tushar, Wayes, Tapan Kumar Saha, Chau Yuen, Thomas Morstyn, Nahid-Al-Masood, H. Vincent Poor, and Richard Bean. "Grid Influenced Peer-to-Peer Energy Trading." IEEE Transactions on Smart Grid 11, no. 2 (March 2020): 1407–18. http://dx.doi.org/10.1109/tsg.2019.2937981.

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3

R, Sitharthan, Sanjeevikumar Padmanaban, Shanmuga Sundar Dhanabalan, and Rajesh M. "Peer-to-peer energy trading using blockchain technology." Energy Reports 8 (November 2022): 2348–50. http://dx.doi.org/10.1016/j.egyr.2022.01.145.

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4

Alvaro-Hermana, R., Jesus Fraile-Ardanuy, Pedro J. Zufiria, Luk Knapen, and Davy Janssens. "Peer to Peer Energy Trading with Electric Vehicles." IEEE Intelligent Transportation Systems Magazine 8, no. 3 (2016): 33–44. http://dx.doi.org/10.1109/mits.2016.2573178.

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5

Ali, Faizan Safdar, Moayad Aloqaily, Omar Alfandi, and Oznur Ozkasap. "Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading." Computer 53, no. 9 (September 2020): 56–65. http://dx.doi.org/10.1109/mc.2020.2991453.

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6

Zhang, Chenghua, Jianzhong Wu, Yue Zhou, Meng Cheng, and Chao Long. "Peer-to-Peer energy trading in a Microgrid." Applied Energy 220 (June 2018): 1–12. http://dx.doi.org/10.1016/j.apenergy.2018.03.010.

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7

Alam, Muhammad Raisul, Marc St-Hilaire, and Thomas Kunz. "Peer-to-peer energy trading among smart homes." Applied Energy 238 (March 2019): 1434–43. http://dx.doi.org/10.1016/j.apenergy.2019.01.091.

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8

Khorasany, Mohsen, Yateendra Mishra, and Gerard Ledwich. "Hybrid trading scheme for peer-to-peer energy trading in transactive energy markets." IET Generation, Transmission & Distribution 14, no. 2 (January 31, 2020): 245–53. http://dx.doi.org/10.1049/iet-gtd.2019.1233.

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9

Wang, Ning, Weisheng Xu, Zhiyu Xu, and Weihui Shao. "Peer-to-Peer Energy Trading among Microgrids with Multidimensional Willingness." Energies 11, no. 12 (November 27, 2018): 3312. http://dx.doi.org/10.3390/en11123312.

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Networked microgrids are emerging for coordinating distributed energy resources in distribution networks in the future Energy Internet, for which developing an efficient energy market model is crucial for facilitating multi-directional trading among microgrids. In this paper, a peer-to-peer energy trading mechanism is presented using non-cooperative bidding among microgrids. Multidimensional willingness, including time pressure and counter behavior for mimicking the personalized behaviors of microgrids, was taken into account in the design of the bidding strategy. Under a parallel trading framework based on a blockchain, the proposed multidimensional willingness bidding strategy turns out to be able to make rational decisions with sufficient flexibility in the bidding process. The simulation results of a realistic case of microgrids from Guizhou Province, China, validate that the proposed peer-to-peer energy trading mechanism is capable of raising the microgrids’ profits and renewable energy source utilization.

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10

Junlakarn, Siripha, Phimsupha Kokchang, and Kulyos Audomvongseree. "Drivers and Challenges of Peer-to-Peer Energy Trading Development in Thailand." Energies 15, no. 3 (February 8, 2022): 1229. http://dx.doi.org/10.3390/en15031229.

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Recent developments in disruptive technologies along with the cost reduction of photovoltaics have been transforming business models in the electricity sector worldwide. The rise of prosumers has led to a more decentralized and open local green energy market through the emergence of peer-to-peer (P2P) energy trading, where consumers and prosumers can buy or sell electricity through an online trading platform. P2P energy trading has the potential to make green energy more accessible at the local level, provide a customer choice that aligns with community values, and promote the use of renewable energy (RE) for local consumption. Although P2P energy trading has already been adopted in some countries, its implementation remains challenging in other countries, including Thailand. In this work, we investigated the drivers and challenges of implementing P2P energy trading in Thailand based on the perspectives of P2P energy trading pilot project developers participating in the regulatory sandbox program. A strategic framework was used to identify the respondents’ standpoints on the political, economic, social, technological, legal, and environmental (PESTLE) factors that can influence the implementation of P2P energy trading. This can help businesses, policymakers, and regulators better understand drivers and barriers of P2P energy trading, which is a potential local energy market. This paper also provides policy recommendations for regulatory changes for the future development of P2P energy trading, including opening a third-party access (TPA) regime, enabling a liberalized market in the electricity market, and integrating the role and responsibilities of the prosumer for P2P energy trading into existing law.

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11

SUCIU, George, Cristian BECEANU, and Andreea BADICU. "A peer-to-peer energy trading model for SMEs." EMERG - Energy. Environment. Efficiency. Resources. Globalization 7, no. 2 (2021): 27–35. http://dx.doi.org/10.37410/emerg.2021.2.02.

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12

Morstyn, Thomas, Alexander Teytelboym, and Malcolm D. Mcculloch. "Bilateral Contract Networks for Peer-to-Peer Energy Trading." IEEE Transactions on Smart Grid 10, no. 2 (March 2019): 2026–35. http://dx.doi.org/10.1109/tsg.2017.2786668.

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13

Zhang, Chenghua, Jianzhong Wu, Chao Long, and Meng Cheng. "Review of Existing Peer-to-Peer Energy Trading Projects." Energy Procedia 105 (May 2017): 2563–68. http://dx.doi.org/10.1016/j.egypro.2017.03.737.

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14

Muhsen, Hani, Adib Allahham, Ala’aldeen Al-Halhouli, Mohammed Al-Mahmodi, Asma Alkhraibat, and Musab Hamdan. "Business Model of Peer-to-Peer Energy Trading: A Review of Literature." Sustainability 14, no. 3 (January 29, 2022): 1616. http://dx.doi.org/10.3390/su14031616.

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Peer-to-peer (P2P) energy trading is a promising energy trading mechanism due to the deployment of distributed energy resources in recent years. Trading energy between prosumers and consumers in the local energy market is undergoing massive research and development, paying significant attention to the business model of the energy market. In this paper, an extensive review was conducted on the current research in P2P energy trading to understand the business layer of the energy market concerning business model dimensions: bidding strategies and the market-clearing approach. Different types of game theoretical-based and auction-based market-clearing mechanisms are investigated, including a detailed classification of auctions. This study considers the possibility of employing the P2P technique in developing countries and reviewing existing business models and trading policies. The business layer of the P2P structure plays a vital role in developing an effective trading mechanism based on interactive energy markets.

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15

Loganathan, Arun S., Vijayapriya Ramachandran, Angalaeswari Sendraya Perumal, Seshathiri Dhanasekaran, Natrayan Lakshmaiya, and Prabhu Paramasivam. "Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions." Energies 16, no. 1 (December 20, 2022): 6. http://dx.doi.org/10.3390/en16010006.

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Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

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van Soest, Henri. "Peer-to-peer electricity trading: A review of the legal context." Competition and Regulation in Network Industries 19, no. 3-4 (September 2018): 180–99. http://dx.doi.org/10.1177/1783591719834902.

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The peer-to-peer (P2P) trading of electricity is a recently developed type of transaction in the electricity system. In a P2P electricity trade, two equal market participants, in most cases prosumers, conclude a contract for the trade of electricity. This article provides a review of the legal context of P2P electricity trading, with a focus on European energy law. Furthermore, the article discusses the relation of P2P electricity trading to the phenomenon of the collaborative economy and to parallel technological developments taking place in the electricity system. We conclude that while the current state of European Union energy law might in principle allow P2P electricity trading, the lack of specific provisions is likely to cause issues in practice.

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17

Morstyn, Thomas, Iacopo Savelli, and Cameron Hepburn. "Multiscale design for system-wide peer-to-peer energy trading." One Earth 4, no. 5 (May 2021): 629–38. http://dx.doi.org/10.1016/j.oneear.2021.04.018.

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18

Gunarathna, Chathuri Lakshika, Rebecca Jing Yang, Sajani Jayasuriya, and Kaige Wang. "Reviewing global peer-to-peer distributed renewable energy trading projects." Energy Research & Social Science 89 (July 2022): 102655. http://dx.doi.org/10.1016/j.erss.2022.102655.

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19

Long, Chao, Yue Zhou, and Jianzhong Wu. "A game theoretic approach for peer to peer energy trading." Energy Procedia 159 (February 2019): 454–59. http://dx.doi.org/10.1016/j.egypro.2018.12.075.

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20

Zhang, Haobo, Hongliang Zhang, Lingyang Song, Yonghui Li, Zhu Han, and H. Vincent Poor. "Peer-to-Peer Energy Trading in DC Packetized Power Microgrids." IEEE Journal on Selected Areas in Communications 38, no. 1 (January 2020): 17–30. http://dx.doi.org/10.1109/jsac.2019.2951991.

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21

Jabbar Aziz Baig, Mirza, M. Tariq Iqbal, Mohsin Jamil, and Jahangir Khan. "Peer-to-Peer Energy Trading in a Micro-grid Using Internet of Things and Blockchain." Electronics ETF 25, no. 2 (December 15, 2021): 39–49. http://dx.doi.org/10.53314/els2125039b.

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With advancements in renewable energy technologies, consumers are becoming prosumers, and renewable energy resources are being used in distributed networks. In an isolated distributed system, peer-to-peer (P2P) energy trading is one of the most promising energy management solutions. In this paper, we propose a P2P energy trading method for micro-grids using open resources and technology. The proposed setup comprises an Internet of Things (IoT) server to transfer energy amongst the peers without human intervention, and an Ethereum based private blockchain is suggested for money transfer in the form of cryptocurrency. The IoT server enables the peers to control and monitor self-produced energy. Arduino UNO, ACS 712 hall-effect current sensor, and a relay are the main components used in the hardware setup. The current sensor data is sent in real- time to Arduino for onward communication to the IoT server. A user-friendly interface has been developed on the server to perform various energy trading tasks. Peers have the choice to access the server remotely to perform energy trading tasks. The energy trading events can be shared amongst peers through e-mail notifications. For financial transactions, we utilized Ganache graphical user interface (GUI) a private Ethereum blockchain eliminating the need for financial institutions. The proposed peer-to-peer energy trading model has been successfully tested for energy trading between two peers. This paper provides details of the proposed hardware and software setup and explains how low-cost P2P energy trading can be achieved.

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22

Wang, Longze, Yu Xie, Delong Zhang, Jinxin Liu, Siyu Jiang, Yan Zhang, and Meicheng Li. "Credible Peer-to-Peer Trading with Double-Layer Energy Blockchain Network in Distributed Electricity Markets." Electronics 10, no. 15 (July 28, 2021): 1815. http://dx.doi.org/10.3390/electronics10151815.

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Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

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23

Merrad, Yaçine, Mohamed Hadi Habaebi, Md Rafiqul Islam, Teddy Surya Gunawan, Elfatih A. A. Elsheikh, F. M. Suliman, and Mokhtaria Mesri. "Machine Learning-Blockchain Based Autonomic Peer-to-Peer Energy Trading System." Applied Sciences 12, no. 7 (March 30, 2022): 3507. http://dx.doi.org/10.3390/app12073507.

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This paper introduces a blockchain-based P2P energy trading platform, where prosumers can trade energy autonomously with no central authority interference. Multiple prosumers can collaborate in producing energy to form a single provider. Clients’ power consumption is monitored using a smart meter that interfaces with an IoT node connected to a blockchain private network. The smart contracts, invoked on the blockchain, enable the autonomous trading interactions between parties and govern accounts behavior within the Ethereum state. The decentralized P2P trading platform utilizes autonomous pay-per-use billing and energy routing, monitored by a smart contract. A Gated Recurrent Unit (GRU) deep learning-based model, predicts future consumption based on past data aggregated to the blockchain. Predictions are then used to set Time of Use (ToU) ranges using the K-mean clustering. The data used to train the GRU model are shared between all parties within the network, making the predictions transparent and verifiable. Implementing the K-mean clustering in a smart contract on the blockchain allows the set of ToU to be independent and incontestable. To secure the validity of the data uploaded to the blockchain, a consensus algorithm is suggested to detect fraudulent nodes along with a Proof of Location (PoL), ensuring that the data are uploaded from the expected nodes. The paper explains the proposed platform architecture, functioning as well as implementation in vivid details. Results are presented in terms of smart contract gas consumption and transaction latency under different loads.

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24

Pu, Liangyi, Song Wang, Xiaodong Huang, Xing Liu, Yawei Shi, and Huiwei Wang. "Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning." Energies 15, no. 24 (December 19, 2022): 9633. http://dx.doi.org/10.3390/en15249633.

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This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community’s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community.

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25

Mehdinejad, Mehdi, Heidarali Shayanfar, and Behnam Mohammadi-Ivatloo. "Peer-to-peer decentralized energy trading framework for retailers and prosumers." Applied Energy 308 (February 2022): 118310. http://dx.doi.org/10.1016/j.apenergy.2021.118310.

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Perger, Theresia, and Hans Auer. "Dynamic participation in local energy communities with peer-to-peer trading." Open Research Europe 2 (January 11, 2022): 5. http://dx.doi.org/10.12688/openreseurope.14332.1.

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Background: Energy communities and local electricity markets (e.g., as peer-to-peer trading) are on the rise due to increasingly decentralized electricity generation and favorable adjustment of the legal framework in many European countries. Methods: This work applies a bi-level optimization model for dynamic participation in peer-to-peer electricity trading to determine the optimal parameters of new participants who want to join an energy community, based on the preferences of the members of the original community (e.g., environmental, economic, or mixed preference). The upper-level problem chooses optimal parameters by minimizing an objective function that includes the prosumers' cost-saving and emission-saving preferences, while the lower level problem maximizes community welfare by optimally allocating locally generated photovoltaic (PV) electricity between members according to their willingness-to-pay. The bi-level problem is solved by transforming the lower level problem by its corresponding Karush-Kuhn-Tucker (KKT) conditions. Results: The results demonstrate that environment-oriented prosumers opt for a new prosumer with high PV capacities installed and low electricity demand, whereas profit-oriented prosumers prefer a new member with high demand but no PV system capacity, presenting a new source of income. Sensitivity analyses indicate that new prosumers' willingness-to-pay has an important influence when the community must decide between two new members. Conclusions: The added value of this work is that the proposed method can be seen as a basis for a selection process between a large number of potential new community members. Most important future work will include optimization of energy communities over the horizon several years.

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Zhang, Bidan, Yang Du, Xiaoyang Chen, Eng Gee Lim, Lin Jiang, and Ke Yan. "A novel adaptive penalty mechanism for Peer-to-Peer energy trading." Applied Energy 327 (December 2022): 120125. http://dx.doi.org/10.1016/j.apenergy.2022.120125.

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28

Tushar, Wayes, Tapan Kumar Saha, Chau Yuen, M. Imran Azim, Thomas Morstyn, H. Vincent Poor, Dustin Niyato, and Richard Bean. "A coalition formation game framework for peer-to-peer energy trading." Applied Energy 261 (March 2020): 114436. http://dx.doi.org/10.1016/j.apenergy.2019.114436.

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29

Abdella, Juhar, and Khaled Shuaib. "Peer to Peer Distributed Energy Trading in Smart Grids: A Survey." Energies 11, no. 6 (June 14, 2018): 1560. http://dx.doi.org/10.3390/en11061560.

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30

Khorasany, Mohsen, Ali Dorri, Reza Razzaghi, and Raja Jurdak. "Lightweight blockchain framework for location-aware peer-to-peer energy trading." International Journal of Electrical Power & Energy Systems 127 (May 2021): 106610. http://dx.doi.org/10.1016/j.ijepes.2020.106610.

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31

Deepa, T., N. Saraswathi, S. Hariprasad, KMS Praveen, P. Elamurugan, and V. Dinesh. "Design and implementation of blockchain based peer to peer energy trading platform." Journal of Physics: Conference Series 2335, no. 1 (September 1, 2022): 012059. http://dx.doi.org/10.1088/1742-6596/2335/1/012059.

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Abstract Blockchain is an emerging technology which is known for its popularity and reliability. First introduced in 2008, it has now expanded to many areas such as IoT, cryptocurrency and Smart contracts. A blockchain consists of blocks assembled in the form of a chain. Data from each participant in the network is stored in these blocks. It is done in a block format called a series of transactions. The Internet of Things (IoT) has transformed many traditional lifestyles. The IoT has enabled cities, housing, pollution control, energy saving and intelligent transportation systems. Blockchain technology and the Internet of Things can increase the efficiency of peer-to-peer energy trading platforms. The open-source peer to peer (P2P) energy trading system is designed on the blockchain. Peer to peer (P2P) is a decentralized communication model in which each party has the same functionality and each party can initiate a communication session. The system collects real time data, monitors and controls it using MQTT Protocol. The trading activities takes place on a web interface which uses a public test Ethereum blockchain, they are tamper-proof. IoT is used to monitor and control the energy. The energy data is acquired and processed using Wi-Fi Based microcontrollers.

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32

Thukral, Manish Kumar. "Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: a review." Clean Energy 5, no. 1 (March 1, 2021): 104–23. http://dx.doi.org/10.1093/ce/zkaa033.

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Abstract Renewable-energy resources require overwhelming adoption by the common masses for safeguarding the environment from pollution. In this context, the prosumer is an important emerging concept. A prosumer in simple terms is the one who consumes as well as produces electricity and sells it either to the grid or to a neighbour. In the present scenario, peer-to-peer (P2P) energy trading is gaining momentum as a new vista of research that is viewed as a possible way for prosumers to sell energy to neighbours. Enabling P2P energy trading is the only method of making renewable-energy sources popular among the common masses. For making P2P energy trading successful, blockchain technology is sparking considerable interest among researchers. Combined with smart contracts, a blockchain provides secure tamper-proof records of transactions that are recorded in distributed ledgers that are immutable. This paper explores, using a thorough review of recently published research work, how the existing power sector is reshaping in the direction of P2P energy trading with the application of blockchain technology. Various challenges that are being faced by researchers in the implementation of blockchain technology in the energy sector are discussed. Further, this paper presents different start-ups that have emerged in the energy-sector domain that are using blockchain technology. To give insight into the application of blockchain technology in the energy sector, a case of the application of blockchain technology in P2P trading in electrical-vehicle charging is discussed. At the end, some possible areas of research in the application of blockchain technology in the energy sector are discussed.

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Elkazaz, Mahmoud, Mark Sumner, and David Thomas. "A hierarchical and decentralized energy management system for peer-to-peer energy trading." Applied Energy 291 (June 2021): 116766. http://dx.doi.org/10.1016/j.apenergy.2021.116766.

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Thomas, Huw, Hongjian Sun, and Behzad Kazemtabrizi. "Closest Energy Matching: Improving peer‐to‐peer energy trading auctions for EV owners." IET Smart Grid 4, no. 4 (March 11, 2021): 445–60. http://dx.doi.org/10.1049/stg2.12016.

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35

Xia, Yuanxing, Qingshan Xu, Haiya Qian, and Li Cai. "Peer-to-Peer energy trading considering the output uncertainty of distributed energy resources." Energy Reports 8 (April 2022): 567–74. http://dx.doi.org/10.1016/j.egyr.2021.11.001.

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36

Wang, Shen, Ahmad F. Taha, Jianhui Wang, Karla Kvaternik, and Adam Hahn. "Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 8 (August 2019): 1612–23. http://dx.doi.org/10.1109/tsmc.2019.2916565.

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Petri, Ioan, Ateyah Alzahrani, Jonathan Reynolds, and Yacine Rezgui. "Federating Smart Cluster Energy Grids for Peer-to-Peer Energy Sharing and Trading." IEEE Access 8 (2020): 102419–35. http://dx.doi.org/10.1109/access.2020.2998747.

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Morstyn, Thomas, and Malcolm D. McCulloch. "Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences." IEEE Transactions on Power Systems 34, no. 5 (September 2019): 4005–14. http://dx.doi.org/10.1109/tpwrs.2018.2834472.

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Kim, SungJoong, YeonOuk Chu, HyunJoong Kim, HyungTae Kim, HeeSeung Moon, JinHo Sung, YongTae Yoon, and YoungGyu Jin. "Analyzing Various Aspects of Network Losses in Peer-to-Peer Electricity Trading." Energies 15, no. 3 (January 18, 2022): 686. http://dx.doi.org/10.3390/en15030686.

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In this study, we examined the impacts of peer-to-peer (P2P) electricity trading on the power losses in the network, which is one of the objectives optimized in the centralized approach. For this purpose, we reviewed the conventional loss management schemes and suggested the requirements to be considered in the design of P2P electricity trading. Then, we described a new loss management framework for P2P transactions and introduced the concept of the transaction guide. Based on the proposed framework, we simulated the P2P transactions with and without the transaction guide and examined the variation in the network losses. Three noteworthy remarks are derived from the simulation in this paper. First, the random characteristics of P2P trading itself do not guarantee favorable transaction ordering in terms of network losses, but when the new loss management framework is applied, the network losses can be effectively decreased. Second, through the new loss management framework, loss costs can be fairly allocated to individual prosumers. Third, to invigorate the P2P electricity trading, an incentive program should be considered to alleviate the burden of loss costs of the first trader in the P2P electricity trading.

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40

Wang, Jiatong, Li Li, and Jiangfeng Zhang. "Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market." International Journal of Electrical Power & Energy Systems 147 (May 2023): 108885. http://dx.doi.org/10.1016/j.ijepes.2022.108885.

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41

Son, Ye-Byoul, Jong-Hyuk Im, Hee-Yong Kwon, Seong-Yun Jeon, and Mun-Kyu Lee. "Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption." Energies 13, no. 6 (March 12, 2020): 1321. http://dx.doi.org/10.3390/en13061321.

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Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.

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Vieira, Guilherme, and Jie Zhang. "Peer-to-peer energy trading in a microgrid leveraged by smart contracts." Renewable and Sustainable Energy Reviews 143 (June 2021): 110900. http://dx.doi.org/10.1016/j.rser.2021.110900.

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43

Etukudor, Christie, Benoit Couraud, Valentin Robu, Wolf-Gerrit Früh, David Flynn, and Chinonso Okereke. "Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets." Energies 13, no. 4 (February 19, 2020): 920. http://dx.doi.org/10.3390/en13040920.

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Abstract:

Reliable access to electricity is still a challenge in many developing countries. Indeed, rural areas in sub-Saharan Africa and developing countries such as India still encounter frequent power outages. Local energy markets (LEMs) have emerged as a low-cost solution enabling prosumers with power supply systems such as solar PV to sell their surplus of energy to other members of the local community. This paper proposes a one-to-one automated negotiation framework for peer-to-peer (P2P) local trading of electricity. Our framework uses an autonomous agent model to capture the preferences of both an electricity seller (consumer) and buyer (small local generator or prosumer), in terms of price and electricity quantities to be traded in different periods throughout a day. We develop a bilateral negotiation framework based on the well-known Rubinstein alternating offers protocol, in which the quantity of electricity and the price for different periods are aggregated into daily packages and negotiated between the buyer and seller agent. The framework is then implemented experimentally, with buyers and sellers adopting different negotiation strategies based on negotiation concession algorithms, such as linear heuristic or Boulware. Results show that this framework and agents modelling allow prosumers to increase their revenue while providing electricity access to the community at low cost.

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Sampath, Lahanda Purage Mohasha Isuru, Yu Weng, Franz-Erich Wolter, Hoay Beng Gooi, and Hung Dinh Nguyen. "Voltage feasibility-constrained peer-to-peer energy trading with polytopic injection domains." Electric Power Systems Research 212 (November 2022): 108591. http://dx.doi.org/10.1016/j.epsr.2022.108591.

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Pena-Bello, Alejandro, David Parra, Mario Herberz, Verena Tiefenbeck, Martin K. Patel, and Ulf J. J. Hahnel. "Integration of prosumer peer-to-peer trading decisions into energy community modelling." Nature Energy 7, no. 1 (December 13, 2021): 74–82. http://dx.doi.org/10.1038/s41560-021-00950-2.

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Lee, Jonathan T., Rodrigo Henriquez-Auba, Bala Kameshwar Poolla, and Duncan S. Callaway. "Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids." IEEE Transactions on Smart Grid 13, no. 1 (January 2022): 702–14. http://dx.doi.org/10.1109/tsg.2021.3122879.

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Yao, Haotian, Yue Xiang, Shuai Hu, Gang Wu, and Junyong Liu. "Optimal Prosumers’ Peer-to-Peer Energy Trading and Scheduling in Distribution Networks." IEEE Transactions on Industry Applications 58, no. 2 (March 2022): 1466–77. http://dx.doi.org/10.1109/tia.2021.3133207.

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Ullah, Md Habib, and Jae-Do Park. "Peer-to-Peer Energy Trading in Transactive Markets Considering Physical Network Constraints." IEEE Transactions on Smart Grid 12, no. 4 (July 2021): 3390–403. http://dx.doi.org/10.1109/tsg.2021.3063960.

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Park, Bo Rang, Min Hee Chung, and Jin Woo Moon. "Becoming a building suitable for participation in peer-to-peer energy trading." Sustainable Cities and Society 76 (January 2022): 103436. http://dx.doi.org/10.1016/j.scs.2021.103436.

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Nguyen, Dinh Hoa. "Electric Vehicle – Wireless Charging-Discharging Lane Decentralized Peer-to-Peer Energy Trading." IEEE Access 8 (2020): 179616–25. http://dx.doi.org/10.1109/access.2020.3027832.

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