A Distributionally Robust Chance-Constrained Unit Commitment with N-1 Security and Renewable Generation

With the increasing penetration of renewable energy generation, one of the major challenges is the problem of how to express the stochastic process of wind power and photovoltaic output as the exact probability density and distribution, in order to improve the security and accuracy of unit commitment results, a distributed robust security-constrained optimization model based on moment uncertainty is proposed, in which the uncertainty of wind and photovoltaic power is captured by two uncertain sets of first- and second-order moments, respectively. The two sets contain the probability distribution of the forecast error of the wind and photovoltaic power, and in the model, the energy storage is considered. In order to solve the model effectively, firstly, based on the traditional chance-constrained second-order cone transformation, according to the first- and second-order moments polyhedron expression of the distribution set, a cutting plane method is proposed to solve the distributed robust chance constraints. Secondly, the modified IEEE-RTS 24 bus system is selected to establish a simulation example, an improved generalized Benders decomposition algorithm is developed to solve the model to optimality. The results show that the unit commitment results with different emphasis on economy and security can be obtained by setting different conservative coefficients and confidence levels and, then, provide a reasonable decision-making basis for dispatching operation.

Automated summary

A distributed robust security-constrained optimization model based on moment uncertainty is proposed to address the stochastic process of wind power and photovoltaic output, with consideration of energy storage. By utilizing a cutting plane method and an improved generalized Benders decomposition algorithm, the model can be effectively solved to obtain unit commitment results with different emphasis on economy and security.