With the increasing penetration of renewable energy (RE), the operations of distribution network are threatened and some issues may appear, i.e., large voltage deviation, deterioration of statistic voltage stability, high power loss, etc. In turn, RE accommodation would be significantly impacted. Therefore, we propose a many-objective distribution network reconfiguration (MDNR) model, with the consideration of RE curtailment, voltage deviation, power loss, statistic voltage stability, and generation cost. This aims to assess the trade-off among these objectives for better operations of distribution networks. As the proposed model is a non-convex, non-linear, many-objective optimization problem, it is difficult to be solved. We further propose a deep reinforcement learning (DRL) assisted multi-objective bacterial foraging optimization (DRL-MBFO) algorithm. This algorithm combines the advantages of DRL and MBFO, and is targeted to find the Pareto front of proposed MDNR model with better searching efficiency. Finally, we conduct case study on the modified IEEE 33-bus, 69-bus, and 118-bus power distribution systems, and results verify the effectiveness of the MDNR model and outperformance of the proposed DRL-MBFO.

Reliability is an overriding concern for power systems that involve different types of uncertainty including contingencies and intermittent renewables. Contingency-constrained unit commitment (CCUC) satisfying the “N - 1 rule” is extremely complex, and the complexity is now compounded by the drastic increase in renewables. This paper develops a novel interval optimization approach for CCUC with N - 1 transmission contingencies and renewable generation. A large number of transmission contingencies are innovatively described by treating corresponding generation shift factors (GSFs) as uncertain parameters varying within intervals. To ensure solution robustness, bounds of GSFs and renewables in different types of constraints are captured based on interval optimization. The resulting model is a mixed-integer linear programming problem. To alleviate its conservativeness and to further reduce the problem size, ranges of GSFs are shrunk through identifying and removing redundant transmission constraints. To solve large-scale problems, Surrogate Lagrangian Relaxation and branch-and-cut (B&C) are used to simultaneously exploit separability and linearity. Numerical results demonstrate that the new approach is effective in terms of computational efficiency, solution robustness, and simulation costs.