Optimizing microgrid operations with consideration of energy conservation and emission reduction benefits in spatial econometrics

This study combines spatial econometric models with intelligent optimization algorithms to explore the spatial distribution characteristics of China's carbon emissions and their optimization and regulation mechanisms. It aims to improve the operational efficiency of regional multi-microgrid systems under the constraints of energy conservation and emission reduction. The Moran index is used to analyze spatial autocorrelation based on China's carbon emission data from 2013 to 2023. The results show that the spatial agglomeration of carbon emissions is strengthening year by year. In addition, the study constructs a three-layer multi-microgrid control system and adopts an improved whale optimization algorithm for scheduling optimization. On 10 standard test functions, the average error value of the improved algorithm is less than 0.0023, which is about 31.4% lower than that of the original algorithm. Meanwhile, the improved algorithm's performance is better than other similar algorithms on most functions. In the actual scheduling simulation, during daytime hours with abundant renewable energy, Microgrid 1 achieves a minimum operating cost of 214.9 yuan, which is 1.3% lower than that of Microgrid 2 (217.65 yuan). Moreover, the environmental emission cost is reduced to 54.47 yuan. This study enhances the low-carbon scheduling capability of multi-microgrid systems; it also provides theoretical support and policy references for realizing regional collaborative emission reduction and the national carbon neutrality strategy.