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This paper proposes a new method for assessing the vulnerability of power systems based on the controllability theories of complex networks. A novel controllability index is established, taking into consideration the full controllability of the power systems, for identifying critical nodes. The network controllability model is used to calculate the minimum number of driver nodes (ND), which can solve the computable problems of the controllability of power systems. The proposed approach firstly applies the network controllability theories to research the power systems' vulnerability, which can not only effectively reveal the important nodes but also maintain full control of the power systems. Meanwhile, the method can also overcome the limitation of the hypothesis that the weight of each link or transmission line must be known compared with the existing literature. In addition, the power system is considered as a directed network and the power system model is also redefined. The proposed methodology is then used to identify critical nodes of the IEEE 118 and 300 bus system. The results show that the failure of the critical nodes can clearly increase ND and lead a significant driver node shift. Thus, the rationality and validity are verified.

This paper presents a novel distributed double-consensus algorithm to solve the optimal energy management problem for multiple energy hubs interconnected with each other. The objective is achieved by establishing two interactive and paralleled consensus procedures modified by their corresponding feedback terms. Meanwhile, a novel projection operation method is proposed to map the infeasible values into the feasible operating region. The proposed algorithm can effectively handle the coupled variables problem existing in the objective function and constraint limits. Moreover, the optimality and convergence analysis are performed strictly under strong connectivity conditions only. Simulations performed on standard test cases are provided to illustrate the effectiveness of the proposed distributed algorithm.

In this paper, a novel distributed method is presented to solve combined heat and power economic dispatch problem, which is formulated as a distributed coupled optimization problem. The optimization goal is achieved by establishing two modified consensus protocols with two corresponding feedback parts while satisfying the electrical and heat supply–demand balance. Moreover, an alternating iterative method is proposed to handle the heat-electrical coupling problem existed in the objective function and the feasible operating regions. In addition, the proposed distributed method is implemented by a multi-agent system framework, which only requires local information exchange among neighboring agents. Simulation results obtained on a 16-bus test system are provided to illustrate the effectiveness of the proposed distributed method.