Open AccessA hierarchical approach for P2P energy trading considering community energy storage and PV‐enriched system operator
Author(s) -
Gokcek Tayfur,
Sengor Ibrahim,
Hayes Barry P.,
Erdinc Ozan
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12636
Subject(s) - renewable energy , bidding , energy storage , environmental economics , computer science , profit (economics) , photovoltaic system , grid , integer programming , scheduling (production processes) , market clearing , linear programming , mathematical optimization , operations research , business , microeconomics , economics , power (physics) , electrical engineering , engineering , mathematics , physics , geometry , algorithm , quantum mechanics
The requirements to reduce dependence on fossil fuels and minimize harmful emissions necessitate using renewable energy more effectively. In this regard, a Peer‐to‐Peer (P2P) energy trading market in which excess power is sold to neighbors is emerging in place of the typical market where excess power is sold back to the grid. In this study, a bi‐level optimal bidding strategy is proposed in which community energy storage systems (CES) and community photovoltaic (CPV) generation are considered. Moreover, individual PV and CES are taken into account for each household. While optimal scheduling of prosumers is carried out at the lower level, the local clearing price considering the profit of Energy Sharing Provider (ESP) is obtained at the upper level. Furthermore, various case studies consisting of Peer‐to‐Grid (P2G), P2P, and CES‐supported P2P are created under different storage capacities. The devised mixed‐integer linear programming (MILP) based model is tested using each case study, validating the proposed model and proving its effectiveness.