Economic dispatch with CHP and wind power using probabilistic sequence theory and hybrid heuristic algorithm

Abstract

The injection of wind power continues to increase rapidly all over the world. Hence, it is necessary for dispatchers to take uncertainties into consideration when they make economic dispatch plans. In northeastern China, because wind power utilization is usually limited by the strong interdependency between the heat and power supply, establishing an optimization model becomes more complex when the power network contains both wind farms and combined heat and power (CHP) units. In this paper, we establish a novel chance constrained programming model based on the probabilistic sequence theory, which redefines the spinning reserve constraints and power flow constraints to make the calculation and analysis easier. A two-stage hybrid heuristic method based on the sequential quadratic programming and genetic algorithm (GA) is proposed in this study. The GA is applied to tackle chance constraints that cannot be transformed into a deterministic model by the quantile method. Moreover, both dynamic economic dispatch and day-ahead economic dispatch are simulated through different cases. Finally, the results demonstrate that to facilitate wind power integration, it may be a good choice to distribute a greater heat load to CHP units with lower efficiency.