Multi-objective optimization based demand response program with network aware peer-to-peer energy sharing

doi:10.1016/j.ijepes.2024.109887

2024-06-01

Abhishek Tiwari *

Bablesh Kumar Jha

Naran M. Pindoriya

DOI：10.1016/j.ijepes.2024.109887

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摘要

En 中文

Demand -side energy management programs, such as Demand Response (DR) and Peer -to -Peer (P2P) energy sharing, have recently begun to be implemented to optimize the integration of distributed clean energy resources and to leverage generation and demand -side flexibility in the distribution system. In general, if not optimally planned, P2P energy sharing may have a negative impact on power distribution utilities' primary objectives, such as minimizing energy losses, flattening the load profile, and improving voltage regulation. The DR program is considered as one of the potential mechanisms to alleviate the possible adverse impact of P2P energy sharing while maintaining the interest of peers participating in decentralized energy sharing. The analysis of the impact of P2P energy sharing with varying peers' physical locations involved utilizing the Voltage Sensitivity Factor (VSF). The influence on the distribution network is contingent upon the difference in VSF values among the individual peers engaged in energy sharing. This study introduced the k -Means algorithm for VSF-based peer clustering, thereby generating the merit order and realizing the network -aware P2P energy transactions for the selected scenarios. A multi -objective optimization model -based real-time pricing -based DR scheme has been formulated to address the adverse impact of P2P energy sharing. Furthermore, this study quantifies the impact of DR potentials, which vary from 5% to 45% in increments of 10%, on the Peak -toAverage Ratio (PAR), overall energy loss, and total energy cost reduction. The modified IEEE 33 bus system incorporating industrial, commercial, and residential consumers' load profile of 24 h at selected buses has been considered to test and validate the proposition.

Keyword:

Demand response

Peer-to-peer energy sharing

DR potential

Energy loss

Peak-to-average ratio

Energy cost

K-means clustering