Optimal allocation of industrial park multi-energy complementary system based on typical scenarios: Case study of Shenzhen

The multi-energy complementary system (MECS) is a new mode that converts renewables into electricity and is usually equipped with hydrogen storage. It realizes flexible conversion of electric and hydrogen energy, achieving high efficiency and low carbon. However, how to optimize capacity of each equipment based on operation strategies and objectives becomes the most important issue. Thus, this study focuses on MECS optimal allocation and manages to work out a scientific framework. Firstly, the Density-Based Spatial Clustering of Applications with Noise algorithm and the Calinski Harabasz Index are firstly integrated to improve K-means clustering algorithm and identify typical scenarios. It can efficiently deal with long-sequence scenario reduction under the uncertainty of generation and load demand. Then, different from conventional objective-determining processes, indicators of the net present value, the carbon emission reduction, the power curtailment rate are taken into account to reflect comprehensive benefits from the life cycle of the MECS. Subsequently, for the difficulty of mixed integer fraction optimization, the-constraint method is introduced in model solving. Finally, a case study of Shenzhen is carried out for verification. The proposed models can achieve optimal allocation with different preference, which can provide theoretical and technical reference for MECS development.