Congestion and Price Prediction in Locational Marginal Pricing Markets Considering Load Variation and Uncertainty

This work investigates the prediction of electricity price and power transmission network congestions under load variation and uncertainty in deregulated power systems. The study is carried out in three stages. In the first stage, the mathematical programming models, which produce the generation dispatch solution, the Locational Marginal Price (LMP), and the system statuses such as transmission congestions, are reviewed. These models are often referred to as Optimal Power Flow (OPF) models, and can be categorized into two major groups: Alternating Current OPF (ACOPF) and Direct Current OPF (DCOPF). Due to the convergence issue with the ACOPF model and the concern of inaccuracy with the DCOPF model, a new DCOPF-based algorithm is proposed, using a fictitious nodal demand (FND) model to represent power losses at each individual line. This is an improvement over the previous work that assigns losses to a few user-defined buses, and is capable of achieving a better tradeoff between computational effectiveness and the accuracy of the results. In the second stage, the solution features are explored for each of the three OPF models to predict critical load levels where a step change of LMP occurs due to the change of binding constraints. After careful examinations of the mathematical relationship of the OPF solutions, nodal prices, and congestions, with respect to load variation, simplex-like method, quadratic interpolation method, and variable substitution method are proposed for each of the three OPF models respectively in order to predict price changes and system congestion. In the last stage, the probabilistic feature of the forecasted LMP is investigated. Due to the step change characteristic of the LMP and uncertainty in load forecasting, the forecasted LMP represents only a certain possibility in a lossless DCOPF framework. Additional possible LMP values exist, other than the deterministically forecasted LMP. Therefore, the concept of Probabilistic LMP is introduced and a systematic approach to quantify the probability of the forecasted LMP, with respect to load variation, is proposed. Similar concepts and methodology have been applied to the ACOPF and FND-based DCOPF frameworks, which can be useful for power market participants in making financial decisions

Study of the quasi-two-body decays B^{0}_{s} \rightarrow \psi(3770)(\psi(3686))\pi^+\pi^- with perturbative QCD approach

In this note, we study the contributions from the S-wave resonances, f_{0}(980) and f_{0}(1500), to the B^{0}_{s}\rightarrow \psi(3770)\pi^ {+}\pi^{-} decay by introducing the S-wave \pi\pi distribution amplitudes within the framework of the perturbative QCD approach. Both resonant and nonresonant contributions are contained in the scalar form factor in the S-wave distribution amplitude \Phi^S_{\pi\pi}. Since the vector charmonium meson \psi(3770) is a S-D wave mixed state, we calculated the branching ratios of S-wave and D-wave respectively, and the results indicate that f_{0}(980) is the main contribution of the considered decay, and the branching ratio of the \psi(2S) mode is in good agreement with the experimental data. We also take the S-D mixed effect into the B^{0}_{s}\rightarrow \psi(3686)\pi^ {+}\pi^{-} decay. Our calculations show that the branching ratio of B^{0}_{s}\rightarrow \psi(3770)(\psi(3686))\pi^ {+}\pi^{-} can be at the order of 10^{-5}, which can be tested by the running LHC-b experiments.Comment: 10 pages, 3 figure

Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE

Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201