Global sensitivity analysis (GSA) can measure the contributions of emerging uncertainty sources as random inputs for the probabilistic static voltage stability assessment (PSVSA). The correlation among these random inputs in power systems is significant and inherent, however, the importance of such correlated uncertainty sources has not been thoroughly and deeply interpreted by the existing GSA methods. To fill this gap, an innovative variance-based GSA approach is proposed herein. At the initial step, orthogonalization independence transformation (OIT) technology is introduced to transform these correlated random inputs into the independent ones with orthogonality. Based on these, a new set of univocal sensitivity indices (SI), including uncorrelated marginal contribution, data correlation contribution, physical interaction contribution, and a whole effect contribution from them together, are defined accordingly. To achieve an acceptable computational burden, two strategies are proposed to determine these novel SI, which are efficient for both low-dimensional and high-dimensional inputs, respectively. At last, the proposed GSA approach is tested on the modified IEEE 5-bus and IEEE 118-bus systems, through which its effectiveness and superiority are verified, compared to the existing GSA approaches in tackling the correlated uncertainty sources of PSVSA.

The rapid development of the electric vehicle industry produces large amounts of retired power lithium-ion batteries, thus resulting in the echelon utilization technology of such retired batteries becoming a research hotspot in the field of renewable energy. The relationship between the cycle times and capacity decline of retired batteries performs as a fundamental guideline to determine the echelon utilization. The cycle conditions can influence the characteristics of the degradation of battery capacity; especially neglection of the SOC ranges of batteries leads to a large error in estimating the capacity degradation. Practically, the limited cycle test data of the SOC ranges of the retired battery cannot support a model to comprehensively describe the characteristics of the capacity decline. In this background, based on the limited cycle test data of SOC ranges, this paper studies and establishes a capacity degradation model of retired batteries that considers the factors affecting the battery cycle more comprehensively. In detail, based on the data-driven method and combined with the empirical model of retired battery capacity degradation, three semi-empirical modeling methods of retired battery capacity degradation based on limited test data of SOC ranges are proposed. The feasibility and accuracy of these methods are verified through the experimental data of retired battery cycling, and the conclusions are drawn to illustrate their respective scenarios of applicability.

This paper proposes a new probabilistic power flow method for the hybrid AC/VSC-MTDC (Voltage Source Control-Multiple Terminal Direct Current) grids, which is based on the combination of ninth-order polynomial normal transformation (NPNT) and inherited Latin hypercube sampling (ILHS) techniques. NPNT is utilized to directly handle historical records of uncertain sources to build the accurate probability model of random inputs, and ILHS is adopted to propagate the randomness from inputs to target outputs. Regardless of whether the underlying probability distribution is known or unknown, the proposed method has the ability to adaptively evaluate the sample size according to a specific operational scenario of the power systems, thus achieving a good balance between computational accuracy and speed. Meanwhile, the frequency histograms, probability distributions, and some more statistics of the results can be accurately and efficiently estimated as well. The modified IEEE 118-bus system, together with the realistic data of wind speeds and diverse consumer behaviors following irregular distributions, is used to demonstrate the effectiveness and superiority of the proposed method.

The maximum static transfer capability (MSTC) can be regarded as a measure of the power transmission ability of power systems in steady-state operation. These emerging uncertainty sources pose a difficult challenge for the efficient analysis and effective control of MSTC. To address this challenge, the dimension-adaptive sparse grid interpolation (DASGI) method is applied with modified boundaries of the collocation points to establish a surrogate model for the faster calculation of the MSTC based on AC optimal power flow (AC-OPF), thus accelerating its probabilistic analysis as well. Subsequently, the regional sensitivity analysis (RSA) is introduced in terms of contribution to the sample mean (CSM) and to the sample variance (CSV) to identify the critical regions of the uncertainty sources. Hence, an adjustment strategy is developed to increase the mean or decrease the variance of MSTC for secure and stable power transmission. Furthermore, the total CSM (TCSM) and total CSV (TCSV) are proposed as indicators to select the most influential random inputs as the preliminary step of RSA, which reduces the analytical burden. The effectiveness of this proposition is illustrated on the modified IEEE 118-bus system and the Polish 2383-bus power system, where the Monte Carlo simulation method, in combination with the AC-OPF-based MSTC model, provides an accuracy reference and a traditional sensitivity index provides the term for comparison.

With the interconnection of asynchronous ac grids through a voltage source converter-based multiterminal direct current (AGs/VSC-MTDC) system and the large-scale integration of renewable energy, it is important to resolve the power flow calculation problems efficiently and robustly for secure and economic operation of such system under stochastic context. Therefore, an improved sequential algorithm for deterministic power flow (DPF) of AGs/VSC-MTDC system is proposed first. This enhances the conventional manner of updating power losses of VSCs, which leads to a one-shot DPF solution of the ac/dc grid, thus boosting the efficiency; while the coupling of ac/dc systems is considered to remain high accuracy. In combination with the efficient DPF method for each sample, probabilistic power flow (PPF) analysis of the AGs/VSC-MTDC system is performed by using Zhao's three-point estimate method (Z-3PEM) with much fewer samples, thus achieving a higher efficiency with good accuracy. In addition, the PPF analysis of AGs/VSC-MTDC systems being more prone to the poor robustness in stochastic scenarios is effectively addressed, benefitting from the decreased nonconvergence of improved DPF and fewer extreme samples of Z-3PEM. Finally, the effectiveness and performance of works are verified through the results on an 11-terminal VSC-MTDC system with three asynchronous ac grids modified from different IEEE test systems.

Probabilistic voltage assessment under multiple contingencies involves numerous network topologies and uncertain operating conditions, resulting in substantial surrogate modeling and computational costs. To address this problem, this paper proposes a voltage-increment-based low-rank (LR) representation with polynomial chaos expansion (PCE), denoted as LR-PCE-ΔV, for a unified multi-contingency voltage assessment. For each model-construction realization, the voltage increment ΔV is calculated as the difference between the post-contingency and pre-contingency bus voltages. The voltage increments under all contingencies are then stacked to form a joint multi-contingency voltage-increment response matrix. Singular value decomposition (SVD) is applied to extract the dominant modes, and one PCE surrogate is constructed for the coefficient of each retained mode. For a new input realization, the predicted modal coefficients are used to reconstruct the voltage increments under all contingencies, after which the corresponding pre-contingency voltages are added to recover the post-contingency voltages. The method is verified on the IEEE 118-bus system with 231 N − 1 contingencies against conventional Monte Carlo simulation (MCS), comprising the pre-contingency AC optimal power flow (ACOPF) and post-contingency AC power flow calculations (ACPF). The proposed method reduces the number of required PCE surrogates from 231 to 21. Compared with the corresponding low-rank model constructed directly from the post-contingency voltages, it reduces the RMSE by 85.9% and achieves a 7.7-fold speedup over conventional MCS while accurately reproducing voltage distributions.