The accelerated penetration rate of renewable energy sources (RES) brings environmental benefits at the expense of increasing operation cost and undermining the satisfaction of the N-1 security criterion. To address the latter issue, this paper envisions N-1 security control in RES dominated power systems through stochastic multi-period AC security constrained optimal power flow (SCOPF). The paper extends the state-of-the-art, i.e. deterministic and single time period AC SCOPF, to capture two new dimensions, RES stochasticity and multiple time periods, as well as emerging sources of flexibility such as flexible loads (FL) and energy storage systems (ESS). Accordingly, the paper proposes and solves for the first time a new problem formulation in the form of stochastic multi-period AC SCOPF (S-MP-SCOPF). The S-MP-SCOPF is formulated as a non-linear programming (NLP) problem. It computes optimal setpoints of flexibility resources and other conventional control means for congestion management and voltage control in day-ahead operation. Another salient feature of this paper is the comprehensive and accurate modelling, using: AC power flow model for both pre-contingency and post-contingency states, inter-temporal constraints for resources such as FL and ESS in a 24-hours time horizon and RES uncertainties. The importance and performances of the proposed model through a direct approach, pushing the problem size up to the solver limit, are illustrated on two test systems of 5 nodes and 60 nodes, respectively, while future work will develop a tractable algorithm.
A Unified Theory for Transient Synchronization Stability Analysis of Renewable Dominated Power Systems
State Key Laboratory of Advanced Electromagnetic Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（华中科技大学电气与电子工程学院先进电磁技术国家重点实验室）；Institute of Physics, Humboldt University Berlin（柏林洪堡大学物理研究所）
Robust Capacity Expansion Modelling for Renewable Energy Systems
Forschungszentrum Jülich GmbH, Institute of Climate and Energy Research（于利希研究中心气候与能源研究所）；RWTH Aachen University, Faculty of Computer Science（亚琛工业大学计算机学院）；University of Siegen, Department of Mechanical Engineering（锡根大学机械工程系）
