AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. It predicts voltages of all buses and then uses them to obtain all remaining variables. A fast post-processing method is developed to enforce generation constraints. The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems. Compared with existing approaches, DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
翻译:未来需要更经常地解决AC-OPF最佳电流问题,以维持稳定和经济运作。为了应对这一挑战,建议采用基于深度神经网络的电压限制方法(EepOPF-V),以找到高计算效率的可行解决办法;预测所有公共汽车的电压,然后利用这些电流获取所有剩余变量;开发一种快速后处理方法,以强化生成限制。DeepOPF-V的效能通过若干IEEE测试系统的案例研究得到验证。与现有方法相比,DeepOPF-V的计算速度达到三个级,在保持解决方案可行性方面业绩更好。