Xiandong Xu

A Reliability Assessment Approach for Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

With the increasing utilization of electric vehicles (EVs), transportation systems and electrical power systems are becoming increasingly coupled. However, the interaction between these two kinds of systems are not well captured, especially from the perspective of transportation systems. This paper studies the reliability of integrated transportation and electrical power system (ITES). A bidirectional EV charging control strategy is first demonstrated to model the interaction between the two systems. Thereafter, a simplified transportation system model is developed, whose high efficiency makes the reliability assessment of the ITES realizable with an acceptable accuracy. Novel transportation system reliability indices are then defined from the view point of EV’s driver. Based on the charging control model and the transportation simulation method, a daily periodic quasi sequential reliability assessment method is proposed for the ITES system. Case studies based on RBTS system demonstrate that bidirectional charging controls of EVs will benefit the reliability of power systems, while decreasing the reliability of EVs travelling. Also, the optimal control strategy can be obtained based on the proposed method. Finally, case studies are performed based on a large scale test system to verify the practicability of the proposed method.

The implications of gas infrastructure in the integrated community energy system

AbstractIntegrated community energy systems have attracted considerable attention due to their capabilities in supporting distributed generation integration and providing various energy services to...

An impact increments-based state enumeration reliability assessment approach and its application in transmission systems

This paper proposes an impact increments-based state enumeration (IISE) reliability assessment approach, specially designed for transmission systems. Firstly, the reliability index calculation formula of the traditional state enumeration technique is transformed into an impact increments-based formation. With the derived formula, the calculation of state probability is simplified and the weight of low order contingency states is increased. Moreover, it is proved in this paper that impact increments of mutually independent high order contingency states, whose outage components are with remote distances, can be eliminated. Due to this fact, the calculation of impacts of most high order contingencies can be reduced without decreasing the precision of reliability indices. When applied to electrical power transmission systems, load variations can also be considered by replacing impact increments by their expectations under various load levels. Thus, annual reliability indices can be obtained by the proposed method. Case studies are performed on the IEEE-118 bus system and a 1354-bus portion of European transmission system from PEGASE project. Results indicate that annual reliability indices can be efficiently obtained with IISE method. Comparing with the traditional state enumeration and Monte Carlo simulation methods, the proposed method is more precise and efficient in various kinds of transmission systems.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines (MTs) are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes, which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of MTs. Considering the time-scale difference of various dynamic processes occurring within MTs, the electromechanical subsystem is treated as a fast quasi-linear process, while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics, and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Cooperative obstacle avoidance using bidirectional artificial potential fields

This paper presents a novel technique for obstacle avoidance and target location using autonomous underwater vehicles. The proposed method uses the concept of bidirectional artificial potential fields in order to cooperatively avoid obstacles whilst travelling to a desired location. A fluid-like formation is presented whereby the vehicles are assigned a separation distance, which they adhere to when not in the process of obstacle avoidance. This distance is free of angular constraints, which allows a more flexible formation than traditional approaches. Although cooperative in nature, the proposed strategy allows all the vehicles to be independently guided by the overall potential field. This technique is useful even when other vehicles fail. Both clockwise and anticlockwise fields are simultaneously created around obstacles, and used by the vehicles to ensure cooperative avoidance around the obstacles. The proposed technique could be used for a number of applications such as mapping/exploration/surface inspection to name a few. Simulation results have been conducted for various scenarios and show the method to be effective.

A continuous time Markov chain based sequential analytical approach for composite power system reliability assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Integrated optimal power flow for distribution networks in local and urban scales

This paper develops an integrated optimal power flow (OPF) tool for distribution networks in two spatial scales. In the local scale, the distribution network, the natural gas network, and the heat system are coordinated as a microgrid. In the urban scale, the impact of natural gas network is considered as constraints for the distribution network operation. The proposed approach incorporates unbalance three-phase electrical systems, natural gas systems, and combined cooling, heating, and power systems. The interactions among the above three energy systems are described by energy hub model combined with components capacity constraints. In order to efficiently accommodate the nonlinear constraint optimization problem, particle swarm optimization algorithm is employed to set the control variables in the OPF problem. Numerical studies indicate that by using the OPF method, the distribution network can be economically operated. Also, the tie-line power can be effectively managed.

Optimal scheduling approach for a combined cooling, heating and power building microgrid considering virtual storage system

In this paper, a building based virtual storage system (VSS) model is developed by utilizing the heat storage characteristics of the building. Then, the VSS is integrated to the optimal scheduling model of the building microgrid (BM) for operation cost reduction. The indoor temperature of the building is adjusted within the customer temperature comfort level range to manage the charging/discharging power of the VSS. Finally, two different types of BM cases under the summer refrigeration scenario are carried out to demonstrate the effectiveness of the proposed optimal scheduling approach. Numerical studies demonstrate that the proposed optimal scheduling approach can make full use of the potential of the VSS and furthermore contribute to the operation cost reduction of the BM, while guarantee the customer temperature comfort level at the same time.