A Power System Optimization Planning Model Considering Renewable Energy Time Synergy

As a consequence of the exponential growth in renewable energy production, the issue of its consumption has become increasingly salient, with a concomitant rise in instances of wind and light abandonment. As research progresses, scholars are increasingly aware of the temporal complementarity between renewable energy generations. This phenomenon presents an opportunity to enhance the power system's overall consumption capacity. However, the input data for capturing this complementarity is characterized by a lengthy time span and high resolution, which presents a significant challenge in optimization model. In this paper, a Markov chain model-based time synergy model for renewable energy is proposed as a means of reducing the computational complexity. The proposed model is capable of accelerating the processing of time-dependent information from historical records, thereby facilitating the generation of representative series. Case studies show that the proposed time synergy model markedly diminishes the discrepancy and the computational burden associated with the approximate solution of the full-time span optimization model, when compared to the traditional models.