Chengjin Ye

Bio: Chengjin Ye is an academic researcher from Zhejiang University. The author has contributed to research in topics: Electric power system & Computer science. The author has an hindex of 9, co-authored 26 publications receiving 212 citations.

A Multi-State Model for Transmission System Resilience Enhancement Against Short-Circuit Faults Caused by Extreme Weather Events

Abstract: Due to global climate change, the effect of extreme weather on power systems has attracted extensive attention. In the prior-art grid resilience studies, the hurricanes or wildfires are mainly defended in terms of expected line damages, while they are prone to trigger short-circuit fault (SCF) evolved with dynamic influence in reality. In this paper, a fragile model is developed to evaluate the nodal SCF probability considering the insulation aging of equipment and extreme weather condition. Then, a response framework for extreme weather events is developed for a transmission system to defend the cascading impacts of expected SCFs. Specifically, switches are shifted to restrain the out-of-range short-circuit currents (SCCs) so that to ensure the SCFs can be removed by circuit breakers, generation rescheduling and load shedding are arranged to maintain the post-fault system transient stability. The above measures are optimized simultaneously by an integrated Mixed-Integer Nonlinear Programming (MINLP). Considering the error or uncertainty of weather event forecasts, a multi-state model is established to provide the most cost-effective grid resilience enhancement scheme, in which the expected urgent adaptions of the initial scheme subject to weather state transition is included in the overall cost. The proposed model and techniques are validated using the IEEE 39-bus New-England test system and realistic meteorological data.

Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II

Abstract: Stability is an important constraint in power system operation and the transient stability constrained optimal power flow (OPF) has always received considerable attention in recent years. In this paper, the defects of the existing models and algorithms around this topic are firstly analyzed, on the basis of which, a multi-objective optimization method is proposed. The basic idea of the proposed method is to model transient stability as an objective function rather than an inequality constraint and consider classic transient stability constrained OPF (TSCOPF) as a tradeoff procedure using Pareto ideology. Second, a master-slave parallel elitist non-dominated sorting genetic algorithm II is used to solve the proposed multi-objective optimization problem, the parallel algorithm shows an excellent acceleration effect and provides a set of Pareto optimal solutions for decision makers to select. An innovative weight assigning technique based on fuzzy membership variance is also introduced for a more scientific and objective optimal solution decision. Case study results demonstrate the proposed multi-objective method has many advantages, compared with traditional TSCOPF methods.

Allocation of Centrally Switched Fault Current Limiters Enabled by 5G in Transmission System

Abstract: The allocation of fault current limiters (FCLs) is increasingly challenging in transmission systems these days. Specifically, the utilized deterministic expected short-circuit fault (SCF) scenarios are prone to cause over-configuration of FCLs. Moreover, the well-established local switching framework (LSF) renders inappropriate FCL switching and may further harm the system safe operation. Aiming at the above deficiencies, a novel 5G-based centralized switch FCL (CSF) framework as well as a method to allocate such flexible FCLs optimally is proposed in this paper. In the proposed CSF, the FCLs are switched by a FCL dispatching (FD) model considering system security constraints of both fault current and voltage sags. By exploiting the fast communication capability of 5G network as well as an off-line fault scanning strategy, the FD model is enabled to give online FCL switching schemes to meet the fast requirement of power system protection. Moreover, considering the probabilistic characteristic of SCFs, a bi-level FCL allocation model is established, in which the upper-level model sites and sizes FCLs considering the installation and expected switching costs while the lower-level model determines the optimal switched FCLs under each specific SCF scenario. Finally, numerical results are provided to verify the proposed allocation model, including its defending effect against SCFs in terms of fault current limiting, voltage sags relieving, as well as its cost-effectiveness.

A Distributed MPC to Exploit Reactive Power V2G for Real-Time Voltage Regulation in Distribution Networks

Abstract: It has been demonstrated theoretically and experimentally that the Vehicle-to-Grid (V2G) enabled electric vehicle (EV) charger is of a reactive power compensation ability with a battery or capacitor connected. To exploit the aggregated reactive power V2G abilities of massively dispersed EV chargers, a distributed model predictive control (DMPC) strategy applying to both balanced and unbalanced distribution networks (DNs) is proposed to integrate them into real-time DN voltage regulation. Firstly, based on the instantaneous power theory and voltage sensitivity matrices, a voltage regulation model considering the reactive response of EV chargers is established without violating the normal EV active charging demands. Then, a completely distributed framework is achieved by DMPC, in which prediction information and calculation results are shared in a Peer-to-Peer (P2P) way to realize the asynchronous broadcast. The proposed model and techniques are validated by numerical results obtained from the IEEE European low voltage test feeder system. The case studies indicate that the proposed DMPC is robust to communication latency (CML) and works effectively in both balanced and unbalanced DNs without any control center, which is a significant advantage for the promotion of real-time reactive power V2G in DNs with irregular user integration and relatively poor communication infrastructure.

A Data-Driven Bottom-Up Approach for Spatial and Temporal Electric Load Forecasting

Abstract: With the rapid urbanization, electrical infrastructure spreads to raw areas without existing loads. How to achieve accurate long-term load forecasts based on land use plans is a realistic problem. On the other hand, load forecasting (LF) should be extended to high spatial resolutions to guide middle- or low-voltage planning and time domain to consider the impacts of distribution generations and diversified users on multi-period system demands. A data-driven bottom-up spatial and temporal LF approach is proposed in this paper to solve these challenges. Land plots are treated as basic LF resolution to describe available multi-attribute data in smart grids and modern cities. Kernel density estimation and adaptive k-means are adopted to aggregate typical load densities and profiles of different land use types. Stacked auto-encoders are utilized to forecast the unknown plot load quantities. The neighbor plot loads are summed up to obtain the estimated loads of larger areas based on clustered load profiles. Case studies demonstrate that the proposed LF is more applicable than benchmark methods both in accuracy and application potential. The estimated hierarchical spatial and temporal results are of great significance to guide load balancing, power system planning, and user integration in different voltage levels.

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The gas transmission problem solved by an extension of the simplex algorithm

Abstract: The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flow-pressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real-world data and compared with alternative solution methods.

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Abstract: Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results.