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High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

In the analysis of cascading outages and blackouts in power systems, risky cascading fault chains should be accurately identified in order to do further block or alleviate blackouts. However, the huge computational burden makes online analysis difficult. In this paper, an online search method for representative risky fault chains based on reinforcement learning and knowledge transfer is proposed. This method aims at promoting efficiency by exploiting similarities of adjacent power flow snapshots in operations. After the “representative risky fault chain” is defined, a framework of tree search based on Markov Decision Process and Q-learning is constructed. The knowledge in past runs is accumulated offline and then applied online, with a mechanism of knowledge transition and extension. The proposed learning based approach is verified on an illustrative 39-bus system with different loading levels, and simulations are carried out on a real-world 1000-bus power grid in China to show the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

With the increasing demand of electricity, the development of the urban power grid has drawn a lot of attention in China. In order to focus on the analysis of an urban power grid, the remaining part of the large-scale power grid used is to be reduced. The accuracy of the equivalence is key to urban power grid research. In this paper, a multistep dynamic equivalent method based on district dividing is proposed. Based on the electrical distance, voltage level, and geographic location, the external system is divided into outer layer, inner layer, and buffer subsystem. Each district is separately reduced by different methods. The proposed technique is illustrated by a real large-scale power grid in the south of China. [ABSTRACT FROM PUBLISHER]

A batch of huge hydropower plants with multiple vibration zones have been developed in China. These vibration zones varying with net head, i.e., head-sensitive, seriously challenge the hydro unit commitment (HUC). This paper presents a MILP model for HUC with a head-sensitive reservoir and multiple vibration zones. The focus of the study is mainly on two net head related constraints, namely the unit performance curves and multiple vibration zones. These constraints are linearized through the use of binary 0-1 integer variables. The practical behavior of the MILP model is demonstrated using real-world case studies. The results demonstrate that the proposed model is computationally efficient and suitable for large hydropower plants with multiple vibration zones, showing its potential practicability and validity for solving the HUC problem. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude. [ABSTRACT FROM AUTHOR]

Due to its brief form and growing popularity, microblogging is becoming people’s favorite choice for seeking information and expressing opinions. Messages received by a user mainly depend on whom the user follows. Thus, recommending users with similar interests may improve the experience quality for information receiving. Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users’ preferences by analyzing the user-generated contents. Moreover, users’ interests are not static, on the contrary, they change as time goes by. Based on such intuitions, in this paper, we propose a temporal-topic model to analyze users’ possible behaviors and predict their potential friends in microblogging. The model learns users’ latent preferences by extracting keywords on aggregated messages over a period of time via a topic model, and then the impact of time is considered to deal with interest drifts. The experimental results of friend recommendations on Sina Weibo, one of the most popular microblogging sites in China, have demonstrated the effectiveness of our model. [ABSTRACT FROM AUTHOR]