scholarly journals Peer to Peer Distributed Energy Trading in Smart Grids: A Survey

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1560 ◽  
Author(s):  
Juhar Abdella ◽  
Khaled Shuaib
Keyword(s):  
Smart Grids ◽  
Peer To Peer ◽  
Distributed Energy ◽  
Energy Trading

scholarly journals P2P model for distributed energy trading, grid control and ICT for local smart grids

2017 ◽  
Author(s):  
Ari Pouttu ◽  
Jussi Haapola ◽  
Petri Ahokangas ◽  
Yueqiang Xu ◽  
Maria Kopsakangas-Savolainen ◽  
...  
Keyword(s):  
Smart Grids ◽  
Distributed Energy ◽  
Energy Trading

scholarly journals Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

2019 ◽  
Vol 49 (8) ◽  
pp. 1612-1623 ◽  
Author(s):  
Shen Wang ◽  
Ahmad F. Taha ◽  
Jianhui Wang ◽  
Karla Kvaternik ◽  
Adam Hahn
Keyword(s):  
Smart Grids ◽  
Peer To Peer ◽  
Energy Trading

scholarly journals Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market

2021 ◽  
Author(s):  
Dawei Qiu ◽  
Jianhong Wang ◽  
Junkai Wang ◽  
Goran Strbac
Keyword(s):  
Reinforcement Learning ◽  
Electricity Market ◽  
Smart Grids ◽  
Public Information ◽  
Economic Benefits ◽  
Peer To Peer ◽  
The Other ◽  
Energy Trading ◽  
Utility Company ◽  
Multi Agent

With increasing prosumers employed with distributed energy resources (DER), advanced energy management has become increasingly important. To this end, integrating demand-side DER into electricity market is a trend for future smart grids. The double-side auction (DA) market is viewed as a promising peer-to-peer (P2P) energy trading mechanism that enables interactions among prosumers in a distributed manner. To achieve the maximum profit in a dynamic electricity market, prosumers act as price makers to simultaneously optimize their operations and trading strategies. However, the traditional DA market is difficult to be explicitly modelled due to its complex clearing algorithm and the stochastic bidding behaviors of the participants. For this reason, in this paper we model this task as a multi-agent reinforcement learning (MARL) problem and propose an algorithm called DA-MADDPG that is modified based on MADDPG by abstracting the other agents’ observations and actions through the DA market public information for each agent’s critic. The experiments show that 1) prosumers obtain more economic benefits in P2P energy trading w.r.t. the conventional electricity market independently trading with the utility company; and 2) DA-MADDPG performs better than the traditional Zero Intelligence (ZI) strategy and the other MARL algorithms, e.g., IQL, IDDPG, IPPO and MADDPG.

scholarly journals Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1321 ◽  
Author(s):  
Ye-Byoul Son ◽  
Jong-Hyuk Im ◽  
Hee-Yong Kwon ◽  
Seong-Yun Jeon ◽  
Mun-Kyu Lee
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Renewable Energy Sources ◽  
Peer To Peer ◽  
Smart Contracts ◽  
Functional Encryption ◽  
Smart Meters ◽  
Trade Surplus ◽  
Energy Trading ◽  
Blockchain Technology

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.

scholarly journals Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

2022 ◽  
Vol 70 (1) ◽  
pp. 1053-1067
Author(s):  
Lijo Jacob Varghese ◽  
K. Dhayalini ◽  
Suma Sira Jacob ◽  
Ihsan Ali ◽  
Abdelzahir Abdelmaboud ◽  
...  
Keyword(s):  
Smart Grids ◽  
Load Forecasting ◽  
Peer To Peer ◽  
Forecasting Model ◽  
Energy Trading ◽  
Optimal Load

scholarly journals An Auction Mechanism for Profit Maximization of Peer-to-Peer Energy Trading in Smart Grids

2019 ◽  
Vol 151 ◽  
pp. 361-368 ◽  
Author(s):  
Jema Sharin PankiRaj ◽  
Abdulsalam Yassine ◽  
Salimur Choudhury
Keyword(s):  
Smart Grids ◽  
Profit Maximization ◽  
Peer To Peer ◽  
Energy Trading ◽  
Auction Mechanism ◽  
For Profit

scholarly journals Minimizing Energy Loss over Distance and Activating the Energy Trading System in Microgrid

2019 ◽  
Vol 260 ◽  
pp. 01003
Author(s):  
Sang Hyeon Lee ◽  
Myeong-in Choi ◽  
SangHoon Lee ◽  
SoungHoan Park ◽  
Sehyun Park
Keyword(s):  
Energy Loss ◽  
Power Line ◽  
Peer To Peer ◽  
Small Scale ◽  
Distributed Energy ◽  
Trading System ◽  
Energy Trading ◽  
Trading Systems ◽  
Efficient Energy ◽  
Electricity Trade

As small-scale distributed energy is gradually expanding, commercialization of peer to peer(P2P) energy trading that freely exchanges energy among individuals in various countries is being commercialized, and the Microgrids (MGs) are considered to be an optimal platform for P2P energy trading. Although conducting electricity trade among individuals without going through power companies is still in its infancy, it is expected to expand gradually as the awareness of the shared economy grows and the MG spreads. Research on more efficient trading systems is needed while trading energy in MG. Therefore we propose a more efficient energy trading system that minimizes the loss in proportion to the distance of the power line when energy trading is performed in the MG. We have constructed a virtual MG environment and experimented with energy trading scenarios. As a result, when the algorithm is applied, loss in proportion to the distance is reduced by 2.495% and energy trading becomes more active. The amount of energy and the number of trades increased by 1.5 times during the energy trading process.

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