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Due to the anticipated proliferation of HVDC links up to and beyond 2020 into transmission systems, it is expected that in some locations, two or more HVDC links will be installed with a short electrical distance between them and will form a multiinfeed (MI)-HVDC scenario for the system. The control and operation of MI-HVDC systems are of particular concern for weak grids such as that likely to be found in the North of Scotland network. It is shown that the ac voltage regulation in the VSC-HVDC link may adversely affect the dynamic performance of the host weak ac system under the MI-HVDC scenario. To overcome this identified limitation associated with the ac voltage controller in the VSC-HVDC link, a new controller modification conforming to the grid code is proposed in this paper. Furthermore, this paper presents a robust control design method for ac voltage control using the fractional order (FO) control design theory. The influences of the proposed FO-based ac voltage controller are assessed alongside various voltage controllers for the representative North Scotland system. It is shown that the FO-based ac voltage controller has favorable effects on the dynamic behavior of weak ac systems with MI-HVDC. [ABSTRACT FROM AUTHOR]

A high level of stochastic dependence (or correlation) exists between different uncertainties (i.e., loads and renewable generation), which is nonlinear and non-Gaussian and it affects power system stability. Accurate modeling of stochastic dependence becomes more important and influential as the penetration of correlated uncertainties (such as renewable generation) increases in the network. The stochastic dependence between uncertainties can be modeled using 1) copula theory and 2) joint probability distributions. These methods have been implemented in this paper and their performances have been compared in assessing the small-disturbance stability of a power system. The value of modeling stochastic dependence with increased renewables has been assessed. Subsequently, the critical uncertainties that most affect the damping of the most critical oscillatory mode have been identified and ranked in terms of their influence using advanced global sensitivity analysis techniques. This has enabled the quantification and identification of the impact of modeling stochastic dependence on the raking of critical uncertainties. The results suggest that multivariate Gaussian copula is the most suitable approach for modeling correlation as it shows consistently low error even at higher levels of renewable energy penetration into the power system. [ABSTRACT FROM PUBLISHER]

This paper critically evaluates a number of sensitivity analysis (SA) techniques to identify the most influential parameters affecting power system small-disturbance stability. SA of uncertain parameters has attracted increased attention with the adoption of deregulated market structure, intermittent energy resources, and new types of loads. Identification of the most influential parameters affecting system stability using SA techniques will facilitate better operation and control with reduced monitoring (only of the parameters of interest) by system operators and stakeholders. In total, nine SA techniques have been described, implemented, and compared in this paper. These can be categorized into three different types: local, screening, and global SA. This comparative analysis highlights their computational complexity and simulation time. The methods have been illustrated using a two-area power system and 68 bus NETS-NYPS test system. The priority ranking of all uncertain parameters has been evaluated, identifying the most critical parameters with respect to the small-signal stability of the test systems. It is shown that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a risk-based probabilistic small-disturbance security analysis (PSSA) methodology for use with power systems with uncertainties. This novel addition to existing dynamic security assessment (DSA) techniques can be used to quantify the small-disturbance stability risks associated with forecasted operating conditions. This approach first establishes the probability density functions (PDFs) for the damping of the critical oscillatory electromechanical modes by modeling the stochastic variation of system uncertainties, such as loading levels, intermittent generation sources, and power flows through voltage-source converter–high-voltage direct current (VSC–HVDC) lines. The produced PDFs are then combined with severity measures (either simple risk matrices or continuous functions) in order to quantify the risk of stability issues for the system associated with the forecasted operating scenario. In addition, the PSSA is used to establish risk-based operational limits and the concept of a probabilistic security margin is introduced to more accurately represent the probabilistic operation of uncertain power systems. The proposed techniques are demonstrated using a multiarea meshed power system incorporating two VSC-HVDC systems, one of which is connected to a large wind farm. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using the Probabilistic Collocation Method (PCM) in power-system studies in the presence of uncertainties. The application of the PCM is illustrated on small disturbance stability studies and voltage stability studies. First, the PCM is used to determine the effects of a supplementary power oscillation damping (POD) controller installed on a VSC-HVDC line in the presences of parameter and operational uncertainties. Second, it is used to calculate the probabilistic distance to voltage collapse in a distribution system based on measurement errors. The PCM is shown to produce accurate statistical results whilst drastically reducing the computational time of the studies in both cases. Additionally, a ranking process based on eigenvalue sensitivity is presented alongside the small disturbance analysis as an appropriate method for reduction of system complexity for extension to large power systems. This technique is validated through the application of the PCM on a large power-system model, yielding accurate probabilistic results. [ABSTRACT FROM PUBLISHER]

The paper presents a robust probabilistic methodology for assessing the performance of power system controllers. In this study, the proposed probabilistic technique is applied to a novel power oscillation damping (POD) controller structure implemented through active power modulation of multiple voltage source converter based high voltage direct current (VSC-HVDC) links within a large heavily meshed network exhibiting multiple inter-area modes. The modal linear quadratic Gaussian (MLQG) controller, as an example of advanced POD, is implemented in both a centralized and a decentralized form for comparison. Following the development and demonstration of the effectiveness of the controller designs, the robust probabilistic performance evaluation is carried out incorporating outage contingencies for generators, lines, and VSC-HVDC lines. The results obtained demonstrate that both controller structures are largely robust to wide ranging operating conditions. For those conditions where controller performance is unsatisfactory, various mitigation techniques are discussed, including the use of classification tools to guide operational limits. A contingency specific controller design is shown to significantly improve performance when robustness cannot be achieved. [ABSTRACT FROM PUBLISHER]