With the development of smart grid, energy management becomes critical for reliable and efficient operation of power systems. In this work, we develop a chance-constrained (CC) energy management model for an islanded microgrid which includes distributed generators (DGs), energy storage system (ESS), and renewable generation such as wind power. The objective function of this model consists of generation cost, emission cost and ESS degradation cost. To capture the uncertainty of renewable generation, a novel ambiguity set is introduced without knowing its probability distribution or exact moment information. Based on the ambiguity set, the chance constraint can be processed with distributionally robust optimization (DRO) method and the energy management problem is reformulated as a tractable second-order conic programming (SOCP) problem. The proposed approach is tested with a case study and simulation results indicate that it is effective and reliable. Moreover, the comparison with the method based on known moment information and some other methods is also conducted to show the performance of the proposed method.

The emergence of a great number of regional planning projects worldwide has considerably increased the complexity and relevance of transmission expansion planning, prompting intensive research and investigation on the formulation and solution. In this paper, the security constrained transmission expansion planning problem is addressed by a branch-and-cut Benders decomposition (BCBD) algorithm. It is a deterministic method where the global optimal solution can be guaranteed in a finite number of iterations. Based on this implementation framework, four acceleration strategies have been employed to enhance the performance. For the validation of accuracy and efficiency, the commercial solver Cplex running on the same platform is introduced for comparison, where four types of mixed-integer linear programming algorithms are discriminated by specifying two pairs of key settings, including dynamic searching and parallel implementation. The superiority of BCBD over Cplex has been validated by case studies, where five benchmark systems ranging from 6 to 300 buses are employed. In addition, performance analysis between BCBD and classical Benders decomposition has also been carried out to distinguish the contribution of branch-and-cut framework and acceleration strategies.

Microgrids play an important role in modern power systems which can integrate different kinds of distributed energy resources (DERs). To deal with the uncertainty from various factors such as renewable generation, robust energy management for microgrids has become a significant problem. In this work, a novel multistage robust energy management model for grid‐connected microgrids is developed which considers the uncertainty of renewable generation and load demand. The multistage energy management problem is complex and computationally difficult. To solve this problem, a robust version of dual dynamic programming method is proposed which includes a forward pass and a backward pass procedure and has a similar framework with the common stochastic dual dynamic programming (SDDP) method. Based on real datasets, a case study is carried out to validate the effectiveness of the proposed model and solution methodology. Numerical results show that the proposed approach can effectively achieve the robust optimal solution, and the comparison with other methods also testifies the advantage of the proposed multistage robust model.