Multi-objective optimization model at park level considering economy and PV uptake rate

To address the issues of low absorption rate and high operating cost in the distributed photovoltaic power generation system within the park, a multi-objective optimization model built upon the foundation of the Internet of Things technology is proposed within the context of this study. Firstly, collect the relevant data of distributed photovoltaic new energy in the park, design a reasonable park-level power scheduling strategy, Secondly, the multi-objective optimization model is established to minimize the system operating cost and maximize the photovoltaic absorption rate. Finally, in order to better solve the established multi-objective optimization model, an improved L-NSGA-III algorithm is proposed. The population crossover and mutation strategies based on adaptive strategies are added to the NSGA-III algorithm, which not only improves the accuracy of the algorithm, but also enhances the stability of the algorithm. The experimental results demonstrate that the proposed model can realize the coordinated operation of photovoltaic power generation system (PV) and distribution network, under the premise that the local consumption of distributed photovoltaic energy is not less than 95%, the operating cost of the system is minimized.