TY - GEN
T1 - Decentralized Peer-to-Peer Electricity Trading Simulation Dashboard
AU - Lit, Asrani
AU - Junaidi, Nazreen
AU - Rufus, Shirley
AU - Arief, Yanuar Zulardiansyah
AU - Masra, Sharifah Masniah Wan
AU - Wanik, Mohd Zamri Che
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026/2/7
Y1 - 2026/2/7
N2 - Peer-to-Peer (P2P) electricity trading has emerged as a key enabler for decentralized energy systems, allowing prosumers with distributed energy resources such as solar photovoltaic (PV) systems and electric vehicles (EVs) to buy and sell electricity directly. This paper presents a lightweight, interactive simulation dashboard for P2P electricity trading, developed using the Streamlit framework. The system models energy generation, demand patterns, dynamic pricing, and matching algorithms to emulate a residential P2P microgrid. Prosumers and consumers are represented with distinct profiles including solar homes, EV homes, hybrid solar-EV homes, and traditional consumers allowing visualization of energy flows and market activity. The dashboard provides real-time charts, automated trading logs, and a simplified market-clearing mechanism, making it suitable for teaching, prototyping, and community microgrid feasibility studies. Results demonstrate the capability of the simulation to model heterogeneous household interactions and visualize trade behavior across daily cycles. This tool offers a foundation for further work incorporating battery storage, blockchain smart contracts, and real-world sensor data.
AB - Peer-to-Peer (P2P) electricity trading has emerged as a key enabler for decentralized energy systems, allowing prosumers with distributed energy resources such as solar photovoltaic (PV) systems and electric vehicles (EVs) to buy and sell electricity directly. This paper presents a lightweight, interactive simulation dashboard for P2P electricity trading, developed using the Streamlit framework. The system models energy generation, demand patterns, dynamic pricing, and matching algorithms to emulate a residential P2P microgrid. Prosumers and consumers are represented with distinct profiles including solar homes, EV homes, hybrid solar-EV homes, and traditional consumers allowing visualization of energy flows and market activity. The dashboard provides real-time charts, automated trading logs, and a simplified market-clearing mechanism, making it suitable for teaching, prototyping, and community microgrid feasibility studies. Results demonstrate the capability of the simulation to model heterogeneous household interactions and visualize trade behavior across daily cycles. This tool offers a foundation for further work incorporating battery storage, blockchain smart contracts, and real-world sensor data.
KW - Distributed Energy Resources
KW - Electric Vehicles
KW - Microgrid
KW - Peer-to-Peer Energy Trading
KW - Renewable Energy
UR - https://www.scopus.com/pages/publications/105037633497
U2 - 10.1109/ACDSA67686.2026.11467723
DO - 10.1109/ACDSA67686.2026.11467723
M3 - Conference contribution
AN - SCOPUS:105037633497
T3 - International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2026
BT - International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2026
Y2 - 5 February 2026 through 7 February 2026
ER -