The unmanned aerial vehicle (UAV) is prevalent in power inspection. However, due to a limited battery life, turbulent wind, and its motion, it brings some challenges. To address these problems, a reinforcement learning-based energy-saving path-planning algorithm (ESPP-RL) in a turbulent wind environment is proposed. The algorithm dynamically adjusts flight strategies for UAVs based on reinforcement learning to find the most energy-saving flight paths. Thus, the UAV can navigate and overcome real-world constraints in order to save energy. Firstly, an observation processing module is designed to combine battery energy consumption prediction with multi-target path planning. Then, the multi-target path-planning problem is decomposed into iterative, dynamically optimized single-target subproblems, which aim to derive the optimal discrete path solution for energy consumption prediction. Additionally, an adaptive path-planning reward function based on reinforcement learning is designed. Finally, a simulation scenario for a quadcopter UAV is set up in a 3-D turbulent wind environment. Several simulations show that the proposed algorithm can effectively resist the disturbance of turbulent wind and improve convergence.

The multi-microgrid integrated energy system offers multiple effective approaches to promote clean energy utilization as well as carbon emission reduction, etc. Combined with stepped carbon trading mechanism, it proposes a multi-microgrid (MMG) system with CCS-P2G integrated flexible operation method for optimal scheduling. Firstly, based on the microgrids’ “source-load” characteristic, electricity sharing between the source and load type microgrids can be achieved. Secondly, the CCS-P2G integrated flexible operation method is proposed, i.e. introducing HFC (Hydrogen Fuel Cell) and HES (Hydrogen Energy Storage) in P2G can enhance the utilization effect of hydrogen. Moreover, introducing carbon storage equipment in CCS, which can effectively solve the situation of unsynchronized operation times of CCS and P2G. Finally, introducing stepped carbon trading mechanism can be a further way of limiting carbon emission. Based on the above, MMG’s optimal dispatch model has established with objective of minimizing total operation cost by considering economic performance and carbon emission of the MMG system. Above optimal dispatch model is a mixed integer linear problem, which is solved using CPLEX solver. Simulation results show that electric energy interaction between source-type and load-type microgrids enhances renewable energy utilization rate. Compared to CCS-P2G cooperative operation method, the single-day carbon emissions and total operation cost of multi-microgrid system using CCS-P2G integrated flexible operation method are reduced by 37.02% and 6.80%, respectively. Meanwhile, when the stepped carbon trading mechanism was introduced, multi-microgrid system’s single-day carbon emissions is reduced to 5.40% compared to when normal carbon trading was considered, further constraining MMG’s carbon emissions.

Different renewable energy resources and energy demands between parks lead to waste of resources and frequent interactions between the regional distribution grid and the larger grid. Hence, an optimal dispatching scheme of the regional integrated energy system group (RIESG), which combines the power-to-gas (P2G) and inter-park electric energy mutual aid, is proposed in this paper to solve this problem. Firstly, for the park integrated energy system (PIES) with various structures, the coupling matrix is used to describe the input-output relationship and coupling form of multiple energy sources in the energy-hub (EH), which linearizes the complex multi-energy coupled system and is more conducive to the solution of the model. Secondly, the electrical coupling relationship of the system is improved by adding P2G to enhance the system’s ability to consume renewable energy. Moreover, the installation cost of P2G is introduced to comprehensively consider the impact of the economic efficiency on the system. Finally, to minimize the network loss of energy flow, the optimal dispatching model of RIESG with P2G conversion is constructed through the electric energy mutual aid among the parks. The simulation shows that compared with the independent operation of each park’s integrated energy system (IES), the proposed optimal dispatching strategy of RIESG achieves the mutual benefit of electric energy among park groups, reduces the dependency on the large power grid, and effectively improves the economy of system groups. In this condition, the renewable energy consumption rate reaches 99.59%, the utilization rate of P2G increases to 94.28%, and the total system cost is reduced by 34.83%.