A robust system model for the photovoltaic in industrial parks considering photovoltaic uncertainties and low-carbon demand response

Against the backdrop of carbon peaking and carbon neutrality initiatives, industrial parks have the potential to mitigate external electricity procurement and reduce carbon emissions by incorporating photovoltaic and energy storage systems. However, the inherent unpredictability in photovoltaic power generation poses notable challenges to the optimal planning of industrial parks. In light of this, the present study proposes a robust planning model for the distribution of photovoltaic and energy storage systems within industrial estates, taking into account uncertainties in photovoltaic output and low-carbon demand response. The primary objective of the model is to minimize the yearly comprehensive cost of the industrial park. It is grounded in the carbon emission flow theory, utilizing dynamic carbon emission factors calculated throughout the year as the pricing basis for real-time electricity rates informed by demand response. Subsequently, historical photovoltaic output data are employed to formulate typical output scenarios and their probability distributions through scenario clustering. These norms and constraints serve to bind the associated uncertainty probabilities. Consequently, a two-stage distribution robust model for the photovoltaic and energy storage system is established, employing a data-driven methodology. The efficacy of the proposed model is substantiated through a case simulation of an industrial park utilizing the CPLEX commercial solver. This approach not only underscores the importance of addressing uncertainties in photovoltaic power generation for industrial park planning but also showcases a practical application of the developed model.