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A finite-time projection neural network to solve the joint optimal dispatching problem of CHP and wind power

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Abstract

This paper constructs an optimal scheduling model for combined heat and power generation units with heat storage and wind power generation considering carbon transaction costs and optimizes the output of each unit to reduce wind curtailment rate, carbon emissions, and total operating costs. In the case of considering transmission loss, the optimal scheduling model subject to actual operation constraints is expressed as a non-convex optimization problem. Based on a sufficient condition, the equivalent problem transformed from the original optimization problem is expressed as a convex problem, and a finite-time reduced-dimensional projection neural network with time-varying parameters is proposed to solve the problem. The proposed neural network can reach convergence in a limited time. The Lyapunov function is used to prove the convergence of the algorithm. Finally, the effectiveness of the designed neural network is verified by numerical simulation. Compared with reduced-dimensional projection neural network and finite-time fixed-parameter reduced-dimensional projection neural network, our neural network has a faster convergence speed.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities (Project No. XDJK2020TY003) and also supported by the Natural Science Foundation of China (Grant No: 62176218).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, China

    Boyu Wei

  2. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China

    Xing He

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Wei, B., He, X. A finite-time projection neural network to solve the joint optimal dispatching problem of CHP and wind power. Neural Comput & Applic 34, 7405–7417 (2022). https://doi.org/10.1007/s00521-021-06867-x

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