Zuhao Fan

This study proposes an improved multi-objective black-winged kite algorithm (MOBKA-QL) integrating Q-learning with adaptive mutation strategies for optimizing multi-objective scheduling in integrated energy systems (IES). The algorithm dynamically selects mutation strategies through Q-learning to enhance solution diversity and accelerate convergence. First, an optimal scheduling model is established, incorporating a carbon capture system (CCS), power-to-gas (P2G), solar thermal, wind power, and energy storage to minimize economic costs and carbon emissions while maximizing energy efficiency. Second, the heat-to-power ratio of the cogeneration system is dynamically adjusted according to load demand, enabling flexible control of combined heat and power (CHP) output. The integration of CCS+P2G further reduces carbon emissions and wind curtailment, with the produced methane utilized in boilers and cogeneration systems. Hydrogen fuel cells (HFCs) are employed to mitigate cascading energy losses. Using forecasted load and renewable energy data from a specific region, dispatch experiments demonstrate that the proposed system reduces economic costs and CO₂ emissions by 14.63% and 13.9%, respectively, while improving energy efficiency by 28.84%. Additionally, the adjustable heat-to-power ratio of CHP yields synergistic economic, energy, and environmental benefits.

A dynamic reconfiguration method based on the improved multi-objective dung beetle optimizer (IMODBO) is proposed to reduce the operating cost of the distribution network with distributed generation (DG) and ensure the quality of the power supply, while also minimizing the number of switch operations during dynamic reconfiguration. First, a multi-objective model of distribution network dynamic reconfiguration with the optimization goal of minimizing active power loss and voltage deviation is established. Secondly, the K-means++ clustering algorithm is used to divide the daily load of the distribution network into periods. Finally, using the IMODBO algorithm, the distribution network is reconstructed into a single period. The IMODBO algorithm uses the chaotic tent map to initialize the population, which increases the ergodicity of the initial population and solves the problem of insufficient search space. The algorithm introduces an adaptive weight factor to solve the problem of the algorithm easily falling into a locally optimal solution in the early stage with weak searchability in the later stage. Levy flight is introduced in the perturbation strategy, and a variable spiral search strategy improves the search range and convergence accuracy of the dung beetle optimizer. Reconfiguration experiments on the proposed method were conducted using a standard distribution network system with distributed power generation. Multiple sets of comparative experiments were carried out on the IEEE 33-nodes and PG&E 69-nodes. The results demonstrated the effectiveness of the proposed method in addressing the multi-objective distribution network dynamic reconfiguration problem.