Optimization of multi-energy collaborative economic operation in plateau zero-carbon parks considering the consumption of wind and solar energy

With the gradual promotion and construction of zero-carbon parks on the plateau, the integration of highly random renewable energy power sources has increased the complexity of multi-energy economic dispatch within the parks. The efficient optimization of economic operation in zero-carbon park multi-energy systems (ZCPMESs) including wind power and photovoltaic power is facing significant challenges. In view of problems such as slow convergence speed, low solution accuracy, and poor algorithm robustness of the existing optimization methods in dealing with the economic operation optimization problem of ZCPMES, this paper proposes a hybrid multi-strategy improved beluga whale optimization (HMIBWO) algorithm specifically designed to address these challenges. By designing an inter-species information sharing mechanism and virtual-guided search strategy, the convergence rate of the algorithm is enhanced while effectively balancing its global search and local development capabilities. Further, adaptive mutation strategy is introduced to avoid the problem of search stagnation in HMIBWO and improve the search efficiency of the algorithm. To evaluate the effectiveness of HMIBWO in the economic operation optimization problem of ZCPMES, the solution capability was first verified on 18 benchmark problems, following which it was applied to two typical scenarios and compared against state-of-the-art heuristic algorithms: conventional and highly random. The experimental results show that the power gap of the HMIBWO optimized scheme can be reduced to 3.79%, and the wind and solar power consumption rates can reach 76.99% and 72.68%, indicating that HMIBWO can be effectively applied to optimize the operation of ZCPMES.