Multiobjective Stochastic Economic Dispatch With Variable Wind Generation Using Scenario-Based Decomposition and Asynchronous Block Iteration

We investigated a multiobjective stochastic economic dispatch (MOSED) problem considering variable wind power integration. We transformed this problem into an equivalent large-scale multiobjective deterministic optimization model based on the scenario method. We simultaneously minimized power purchase costs and polluting gas emissions. We introduced the normal boundary intersection (NBI) method to convert the multiobjective optimization (MOO) model into a series of single-objective optimization (SOO) problems, which we solved using the interior-point method (IPM). In the process used to solve each SOO problem, we rearranged the coefficient matrix of the correction equation in the block bordered diagonal form (BBDF) according to the sequence of the forecast scenario and sampling scenarios. Thus, we were able to decompose this correction equation further into a number of low-dimensional equations corresponding to the forecast scenario and sampling scenarios, respectively, and solve them using the asynchronous block iteration method. Furthermore, we implemented the proposed algorithm on an IEEE 39-bus system and a real-provincial power system, and built a parallel computational framework on high-performance clusters to demonstrate the enhancements in computational speed and the reduced memory requirements obtained by parallelization. Through this framework, one can obtain scheduling of the outputs of generators on a day-ahead basis.