Distributed peer-to-peer energy trading framework with manufacturing assembly process and uncertain renewable energy plants in multi-industrial micro-grids

In this paper, a peer-to-peer energy trading framework for multi-industrial micro-grids is proposed to promote the efficient utilization of local renewable energy resources. There are two levels in the proposed framework. In the upper level, each industrial micro-grid chooses to sell or buy electricity with neighbors by responding to renewable energy generation, where the manufacturing sequences within typical assembly processes are characterized as deferrable loads, thereby offering flexible adjustments for energy trading. Simultaneously, the intricate spatiotemporal coupling between production and assembly lines is thoroughly contemplated and formulated as two distinct sets of constraints. Moreover, uncertainties stemming from renewable energy are articulated through a joint distributionally robust chance constraint, and are subsequently transformed utilizing the Wasserstein metric. In the lower level, the non-cooperative game in market participants is conceptualized to determining the price of energy trading. Following this, a distributed hybrid algorithm combining improved constrained differential evolution and neurodynamic approach is introduced. This innovative blend not only markedly augments the quality of the solutions but also proficiently addresses the continuous distributed peer-to-peer energy trading problem. In the end, the case studies illustrate the effectiveness of the proposed hybrid algorithm and highlight the imperative for micro-grids to engage in energy trading.