Publication

Abstract : Determining a fair and secure energy trading price is an important concern in smart grid energy trading networks, where participants interact with each other and the grid using bi-directional communication links. Moreover, the information shared on these communication links is prone to possible vulnerabilities, such as man-in-the-middle attacks, where an intruder sniffs the information, alters it, and gains undue benefit by falsely reporting the participants’ data. To overcome this issue, the proposed model offers a promising approach to address these challenges by introducing a homomorphic encryption-based privacy-preserving model that ensures the privacy of the participants’ data. Furthermore, without exposing the financial conditions of the participants and by utilizing the concept of reserved pricing, a sequential quadratic optimization-based model is proposed to determine the final energy trading price. This is done in such a way that the final price always adheres to the participants’ reserved prices while keeping the real-time supply-to-demand ratio in account. Moreover, the stochastic generation of renewable sources also poses challenges to ensuring fairness in the energy market, where several participants are treated as non-trader entities. For this purpose, the proposed model offers a Euclidean distance-based energy allocation policy to establish fairness in the energy allocation process. The simulation results of the proposed model reveal that it can reduce the energy bills of buyers by 6.53% to 22.11% and boost the revenues of sellers by 6.23% to 32.80% compared to other state-of-the-art models.