Complementary multi-energy power generation systems are a promising solution for multi-energy integration and an essential tool for diversifying renewable energy sources. Despite many studies on developing hybrid renewable energy systems, more research is still needed on applicable models or practical methods. Meta-heuristic algorithms such as the Firefly algorithm are becoming increasingly popular in optimizing hybrid renewable energy systems because they provide fast, accurate, and optimal solutions. Considering the natural complementarity and instability of wind and solar energy, the advantage of pumped storage power plants’ “peak adjustment and valley adjustment”, as well as the grid’s need for a stable and reliable energy supply, the objective of this study is to economically optimize the design of wind-PV pumped storage complementary generation system scheduling with a two-generation Firefly algorithm based on spatial adaptive and Levy’s flight improvement, in comparison with a variety of cutting-edge population intelligence optimization algorithms (GA, PAO, DE, WOA, FA) were compared and analyzed. The impact of pumped storage plants on economic and stabilization objectives is explored. The results show that several meta-heuristics are effective in finding the optimal design. However, the improved Firefly algorithm with an objective function value of 7.8331 is superior to several other algorithms by enhancing the wind and PV benefits while suppressing the output fluctuations of the system. After the construction of the additional pumped storage plant, the output fluctuation of the complementary operation system is only 9.7% of that of the wind power and PV in stand-alone operation after the multi-energy coordination and optimal scheduling. This demonstrates the effectiveness of the optimization method used in this paper. The results of this study can provide a reference for the complementary optimization of pumped storage power plants for intermittent renewable energy sources.

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.

Grid operation and inspection a key links to ensure the safe operation of the power system, which requires efficient task allocation and resource scheduling. To address this problem, this paper proposes a resource scheduling model for grid operation and inspection based on bi-level programming. Firstly, the O&I process is analyzed and defined as a combined optimization problem of the multiple traveling salesman problem (MTSP) and the job-shop scheduling problem (JSP). Secondly, a bi-level programming model of MTSP and JSP is established according to the characteristics of the problem. Finally, an adaptive genetic algorithm is used to solve the problem. The feasibility of the model and the advancement of the algorithm are verified through the simulation of real scenarios and a large number of tests, which provide strong support for the sustainable development of the power system.

As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation.

Upper limb exoskeleton is gradually used in industrial production because of its flexibility, safety, and ease of wear. To improve the load reduction effect of the upper limb exoskeleton in power grid operation, this paper first establishes an efficiency optimization model considering the number of gears, transmission ratio, and pressure angle of upper limb exoskeleton transmission joint gears. Further, an exoskeleton load reduction model based on the joint optimization of multiple parameters of the upper limb exoskeleton is constructed by combining the muscle force application fatigue evaluation. For the above model, a hybrid simulated annealing-flash connection process algorithm based on Cubic mapping and golden sine is designed for the solution. To address the shortcomings of the standard LAPO, Cubic mapping, simulated annealing algorithm, and golden sine operator are introduced for improvement, producing high-quality initial solutions through the traversal of Cubic chaotic sequences, hybrid simulated annealing algorithm to enhance the global search capability of the algorithm, and further incorporating golden sine operator to strengthen the local search capability of the algorithm. In the simulation section, the designed Improved Lightning Connection Process Algorithm (ILAPO) is compared horizontally with cutting-edge swarm intelligence algorithms such as LAPO, PSO, and HBA to verify the feasibility of the model in this paper in the exoskeleton domain and the efficiency of the designed algorithm.