A combined optimization of the sizing and the energy management of an industrial multi-energy microgrid: Application to a harbour area

Abstract

• Sizing and operation combined optimization is proposed for an industrial microgrid. • Multi-energy microgrid is studied, with electricity and hydrogen as energy vectors. • The proposed objective functions aim to maximize incomes while minimizing expenses. • Ways of valorisation are compared: self-consumption, electricity and hydrogen sale. • The sizing and economical results are sensitive to investment and price assumptions. The current evolution of technical and environmental regulations and the recent development of hydrogen mobility require technological breakthroughs in the industrial areas, such as in harbours. Thus, multi-energy microgrids integrating low-carbon energy sources, electrical and hydrogen loads and storage solutions have to be designed. To improve their economic viability, the microgrid components must be carefully sized and the energy must be distributed in the most cost-effective way at any time. So as to foster the expansion of multi-energy microgrids in industrial areas, we propose in this paper a two-level optimization for the energy management and the sizing, applied to an original multi-energy scenario considering electricity and hydrogen as energy vectors. The designed energy management optimization takes into account an objective of economic profitability and constraints related to the availability of the sources and storage solutions, their reliability and the costs. The sizing optimization aims to propose different solutions allowing the benefits to be maximized and the expenses to be minimized. One of the main contributions of the paper is to compare the possible ways of valorisation of the energy available in an industrial microgrid. The results show that the sale of hydrogen allows income to be increased, in addition to self-consumption and sale to the electrical grid, but the electrolyzer involves high investments costs. Finally, a sensitivity analysis is presented and shows that the investment costs of electrolyzer and battery and the hydrogen selling price are key points in the optimal design.