With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. The results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

Microgird (MG) can operate in islanded mode and serve as black-start resource for power system restoration (PSR). In this work, a model predictive control based generator start-up optimization strategy for PSR is proposed utilizing MGs as black-start resources. Firstly, the generator start-up sequence (GSUS) optimization is formulated as a mixed integer linear programming. Then the uncertainties of microgrid black-start resources (MBSRs) are modeled by discretizing the probability distribution of the forecast errors, and representative scenarios for MBSRs extracted by formulating the probability mass transportation problem. Thirdly, the generator start-up optimization strategy considering MBSRs is formulated utilizing the model predictive control (MPC) technique, in which the optimization objective is to maximize the energy capability of the power systems and minimize the load curtailment of the MGs in each looking-ahead interval. Simulations on the IEEE 118 bus system with MGs and Zhejiang provincial power system in China verify that the proposed strategy for PSR can successfully restore the power system and effectively determine the optimal GSUS.

Converting renewable energy into ammonia has been recognized as a promising way to realize “green hydrogen substitution” in the chemical industry. However, renewable power to ammonia (RePtA) requires an essential investment in facilities to provide a buffer against the strong volatility of renewable energy and the limited flexibility of ammonia synthesis, which involves the three main stakeholders, namely, power, hydrogen, and ammonia. Therefore, the sizing and pricing of RePtA play a core role in balancing the interest demands of investors. This article proposes an optimal sizing and pricing method for grid-connected RePtA systems planning. First, power to ammonia (P2A) is modeled as a flexible load, especially considering the limited flexibility of ammonia synthesis, which has been verified using real dynamic regulation data. Second, the multi-investor economic (MIE) model is established considering both external and internal trading modes. Then, a two-stage decomposed sizing and pricing method is proposed to solve the problem caused by the strong coupling of planning, operation, and trading. The information gap decision theory (IGDT) method is utilized to handle the uncertainty of renewable generation. Finally, real data from a real-life system in Inner Mongolia are utilized to verify the proposed approach. The results show that the system proposed has an internal rate of return (IRR) of 8.55%, which indicates the practical feasibility of the proposed planning method.

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F-statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

In recent years, the penetration of hydrogen energy in energy systems has increased dramatically. Hydrogen energy and its related infrastructure, such as hydrogen refueling stations (HRSs) and mobile fuel cell vehicles (MFCVs), exhibit great potential for enhancing the self-healing ability of DSs. In this work, a two-layer hierarchical strategy for improving the restorative self-healing ability of hydrogen-penetrated DS (HPDS) with multiple microgrids (MGs) and HRSs is proposed considering their interactions and MERs such as mobile energy storages and MFCVs. In the lower layer, an optimal energy management strategy is presented for MGs considering the coordination between hydrogen and electricity via the electrolyzer and fuel cell in the HRSs. In the upper layer, an energy sharing (ES) strategy is proposed for the distribution system operator (DSO) to dispatch MERs in order to minimize the power imbalance among MGs and reduce the unserved load. The concept of the equivalent time network (ETN) is introduced, and based on the ETN, a mixed integer linear programming (MILP) model is innovatively proposed to represent the traveling pattern of MERs and to optimize their ES path among MGs. Finally, the two-layer hierarchical strategy is formulated as MILP models and solved using the commercial solver. Case studies verify the effectiveness of the proposed strategies.

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality.