Intelligent decision optimization for energy control of direct current power distribution system with multi-port access for intelligent buildings

The structure of direct current (DC) appliances is simpler and more energy-efficient than that of alternating current (AC) appliances. With the proliferation of novel DC loads (e.g., e-bikes and electric vehicles), DC loads will dominate the loads in buildings. It is necessary to develop a DC power distribution method that better accommodates renewable energy, effectively improves the working efficiency of loads, and significantly enhances energy utilization efficiency. However, there is not yet a unified design for the multi-port converter of DC microgrids. The experience is severely lacking in the overall energy planning for buildings from the perspectives of economy and environ-mental friendliness. To solve the problems, this paper explores the intelligent decision optimization for energy control in DC power distribution system with multi-port access for intelligent buildings. Firstly, a feature analysis was carried out on the multi-port converter of the DC power distribution system for intelligent buildings. With economy and environmental friendliness as the goals, the authors designed constraints related to energy output power, power balance, and distribution node voltage, and established an intelligent decision optimization model for the energy control of the DC microgrid for intelligent buildings. The model was combined with the multi-attribute decision -making framework into a decision-making framework for the coordinated operation of distributed energy supply units, which is based on the interval evidential reasoning (IER). Finally, the authors proposed an evaluation method for the decision-making of the energy control of DC power distri-bution for intelligent buildings, based on the Maclaurin symmetric mean (MSM) operator, and the IER-based decision-making framework. The proposed method was proved effective through exper-iments.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).