Distributed neurodynamic-based economic dispatch strategy for we-energy

Rufei Ren, Yushuai Li, Qiuye Sun, Jing Dai, Huaguang Zhang

Abstract: This paper investigates the distributed collaborative energy management of distributed cyber-energy system with consideration of multiple types of coupled energy sources and loads. Firstly, the energy structure of We-Energy (WE) and the characteristics of multi-energy interconversion are studied. With this effort, the concept of general loads are modeled based on transform loss and compensation coefficient, which enables multiple controllable loads interconverting with each other to reduce energy consumption. As a result, the system capacity costs can be greatly reduced. Furthermore, a new fully distributed neurodynamics-based algorithm is proposed to solve the economic dispatch problem of multiple WEs. The proposed method can only use seldom shared variables to obtain the optimal solutions and does not require the objective function to be strictly convex and smooth in solving convex optimization problems. Meanwhile, the proposed possesses faster convergence speed by employing application-specific integrated circuits. Moreover, we prove that the proposed neurodynamics-based algorithm can asymptotically converge to the global optimal point based on Lyapunov analysis method. Finally, simulation results demonstrate the validity of the algorithm and the effect of general load on the overall operating cost of the WE system.