AUTHOR=Wang Hui , Liao Xu , Ji Xiu , Gong Shanggao , Meng Xiangping , Wang Jiarui 

TITLE=Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1265906

DOI=10.3389/fenrg.2023.1265906

ISSN=2296-598X

ABSTRACT=With the continuous infusion of renewable energy sources, smart buildings have derived from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, leading to complexity and deviation from the time-of-use tariffs set by electricity markets, consequently resulting in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework employing intelligent optimization algorithms, this paper proposes a pricing strategy of introducing retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. Firstly, the independent operation model of intelligent buildings and the electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, aiming to minimize alliance costs, a novel energy-sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy-sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, the paper conducts simulation analyses concerning key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, the market supervision coefficient, and the discount coefficient associated with the grid's sale of electricity to suppliers. Through these analyses, the paper further validates the efficacy of the proposed strategy.