Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids——An interview with Zhou Xiaoxin, chief scientist of "973 Program"

At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.

Taking the Way of “Going Out”——SGCC Got the Franchise for Operating Philippine Power Grid

For Chinese power enterprises,investing overseas and actively seeking international cooperation in energy resources for mutual benefit accords with not only the desires of themselves to become bigger and stronger,but also China's energy strategy. Having won the first overseas state-level power-grid operation right,the State Grid Corporation of China (SGCC) took the lead in carrying out the strategy of "going out."

Integrating high levels of variable renewable energy into electric power systems

As more variable renewable energy(VRE)such as wind and solar are integrated into electric power systems,technical challenges arise from the need to maintain the balance between load and generation at all timescales.This paper examines the challenges with integrating ultrahigh levels of VRE into electric power system,reviews a range of solutions to these challenges,and provides a description of several examples of ultra-high VRE systems that are in operation today.

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.