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With the advancement of supply-side reforms in the energy and power fields, a comprehensive energy system that integrates various energy sources such as electricity, heat, and natural gas has become the main development trend. Based on this, this paper proposes a combined heat and power(CHP) microgrid model with renewable energy. Based on the improved particle swarm algorithm, the optimal scheduling problem of the microgrid model is calculated, and a feasible optimal scheduling strategy considering both economy and low carbon is proposed. Finally, the economy and correctness of the optimal scheduling strategy proposed in this paper are verified by simulation.

To decrease wind curtailment rate and increase wind power accommodation, power purchase area should increase wind power purchase. However, the current electricity price of wind power is higher than that of thermal power, which makes wind power accommodation difficult to quantify. It is not practical to optimize these two objectives of the wind power accommodation and power purchase cost. Based on this contradiction, this paper describes this contradiction by constructing a multi-objective optimization model (MOOM) that considers the above two objectives. Firstly, the model is resolved by hybrid particle swarm optimization and gravity search algorithm (HPSO-GSA) to obtain Pareto optimal solution set. Then, according to fuzzy satisfaction function, Pareto optimal solution is selected from Pareto optimal frontier. Finally, the typical daily load and wind farm output value in spring of a certain region are taken as examples for simulation verification. It is verified that the proposed method can indeed provide a scientific power purchase scheme for regional power purchase.

Cooperative tracking control problem of multiple water–land amphibious robots is discussed in this article with consideration of unknown nonlinear dynamics. Firstly, the amphibious robot dynamic model is formulated as an uncoupled nonlinear one in horizontal plane through eliminating relatively small sway velocity of the platform. Then cooperative tracking control algorithm is proposed with a two-stage strategy including dynamic control stage and kinematic control stage. In dynamic control stage, adaptive consensus control algorithm is obtained with estimating nonlinear properties of amphibious robots and velocities of the leader by neural network with unreliable communication links which is always the case in underwater applications. After that, kinematic cooperative controller is presented to guarantee formation stability of multiple water–land amphibious robots system in kinematic control stage. As a result, with the implementation of graph theory and Lyapunov theory, the stability of the formation tracking of multiple water–land amphibious robots system is proved with consideration of jointly connected communication graph. At last, simulations are carried out to prove the effectiveness of the proposed approaches.