Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Probabilistic load flow calculation for AC traction supplies with AT feeding

Power load flow analysis is essential for system planning, operation, development and maintenance. Its application on railway supply system is no exception. Railway power supplies system distinguishes itself in terms of load pattern and mobility, as well as feeding system structure. An attempt has been made to apply probability load flow (PLF) techniques on electrified railways in order to examine the loading on the feeding substations and the voltage profiles of the trains. This study is to formulate a simple and reliable model to support the necessary calculations for probability load flow analysis in railway systems with autotransformer (AT) feeding system, and describe the development of a software suite to realise the computation.

Probabilistic load flow in AC electrified railways

System analysis within the traction power system is vital to the design and operation of an electrified railway. Loads in traction power systems are often characterised by their mobility, wide range of power variations, regeneration and service dependence. In addition, the feeding systems may take different forms in AC electrified railways. Comprehensive system studies are usually carried out by computer simulation. A number of traction power simulators have been available and they allow calculation of electrical interaction among trains and deterministic solutions of the power network. In the paper, a different approach is presented to enable load-flow analysis on various feeding systems and service demands in AC railways by adopting probabilistic techniques. It is intended to provide a different viewpoint to the load condition. Simulation results are given to verify the probabilistic-load-flow models.

Load flow in electrified railway

Probabilistic load flow techniques have been adopted in AC electrified railways to study the load demand under various train service conditions. This paper highlights the differences in probabilistic load flow analysis between the usual power systems and power supply systems in AC railways; discusses the possible difficulties in problem formulation and presents the link between train movement and the corresponding power demand for load flow calculation.

Smart coordinated distribution system control to enable high level penetration of rooftop PVs

This thesis addresses voltage violation problem, the most critical issue associated with high level penetration of photovoltaic (PV) in electricity distribution network. A coordinated control algorithm using the reactive power from PV inverter and integrated battery energy storage has been developed and investigated in different network scenarios in the thesis. Probable variations associated with solar generation, end-user participation and network parameters are also considered. Furthermore, a unified data model and well-defined communication protocol to ensure the smooth coordination between all the components during the operation of the algorithm is described. Finally this thesis incorporated the uncertainties of solar generation using probabilistic load flow analysis.

Induction motor load impact on power system eigenvalue sensitivity analysis

Load modelling plays an important role in power system dynamic stability assessment. One of the widely used methods in assessing load model impact on system dynamic response is parametric sensitivity analysis. A composite load model-based load sensitivity analysis framework is proposed. It enables comprehensive investigation into load modelling impacts on system stability considering the dynamic interactions between load and system dynamics. The effect of the location of individual as well as patches of composite loads in the vicinity on the sensitivity of the oscillatory modes is investigated. The impact of load composition on the overall sensitivity of the load is also investigated.

Fault isolation in distributed generation connected distribution networks

Protection of a distribution network in the presence of distributed generators (DGs) using overcurrent relays is a challenging task due to the changes in fault current levels and reverse power flow. Specifically, in the presence of current limited converter interfaced DGs, overcurrent relays may fail to isolate the faulted section either in grid connected or islanded mode of operation. In this paper, a new inverse type relay is presented to protect a distribution network, which may have several DG connections. The new relay characteristic is designed based on the measured admittance of the protected line. The relay is capable of detecting faults under changing fault current levels. The relay performance is evaluated using PSCAD simulation and laboratory experiments.

Risk control in transmission system expansion planning with wind generators

With the advent of large-scale wind farms and their integration into electrical grids, more uncertainties, constraints and objectives must be considered in power system development. It is therefore necessary to introduce risk-control strategies into the planning of transmission systems connected with wind power generators. This paper presents a probability-based multi-objective model equipped with three risk-control strategies. The model is developed to evaluate and enhance the ability of the transmission system to protect against overload risks when wind power is integrated into the power system. The model involves: (i) defining the uncertainties associated with wind power generators with probability measures and calculating the probabilistic power flow with the combined use of cumulants and Gram-Charlier series; (ii) developing three risk-control strategies by specifying the smallest acceptable non-overload probability for each branch and the whole system, and specifying the non-overload margin for all branches in the whole system; (iii) formulating an overload risk index based on the non-overload probability and the non-overload margin defined; and (iv) developing a multi-objective transmission system expansion planning (TSEP) model with the objective functions composed of transmission investment and the overload risk index. The presented work represents a superior risk-control model for TSEP in terms of security, reliability and economy. The transmission expansion planning model with the three risk-control strategies demonstrates its feasibility in the case study using two typical power systems

Voltage unbalance improvement in low voltage residential feeders with rooftop PVs using custom power devices

Voltage unbalance is a major power quality problem in low voltage residential feeders due to the random location and rating of single-phase rooftop photovoltaic cells (PV). In this paper, two different improvement methods based on the application of series (DVR) and parallel (DSTATCOM) custom power devices are investigated to improve the voltage unbalance problem in these feeders. First, based on the load flow analysis carried out in MATLAB, the effectiveness of these two custom power devices is studied vis-à-vis the voltage unbalance reduction in urban and semi-urban/rural feeders containing rooftop PVs. Their effectiveness is studied from the installation location and rating points of view. Later, a Monte Carlo based stochastic analysis is carried out to investigate their efficacy for different uncertainties of load and PV rating and location in the network. After the numerical analyses, a converter topology and control algorithm is proposed for the DSTATCOM and DVR for balancing the network voltage at their point of common coupling. A state feedback control, based on pole-shift technique, is developed to regulate the voltage in the output of the DSTATCOM and DVR converters such that the voltage balancing is achieved in the network. The dynamic feasibility of voltage unbalance and profile improvement in LV feeders, by the proposed structure and control algorithm for the DSTATCOM and DVR, is verified through detailed PSCAD/EMTDC simulations.

A three-phase to single-phase matrix converter based bi-directional IPT system for charging electric vehicles

This paper presents a novel three-phase to single-phase matrix converter (TSMC) based bi-directional inductive power transfer (IPT) system for vehicle-to-grid (V2G) applications. In contrast to existing techniques, the proposed technique which employs a TSMC to drive an 8th order high frequency resonant network, requires only a single-stage power conversion process to facilitate bi-directional power transfer between electric vehicles (EVs) and a three-phase utility power supply. A mathematical model is presented to demonstrate that both magnitude and direction of power flow can be controlled by regulating either relative phase angles or magnitudes of voltages generated by converters. The viability of the proposed mathematical model is verified using simulated results of a 10 kW bi-directional IPT system and the results suggest that the proposed system is efficient, reliable and is suitable for high power applications which require contactless power transfer.

Controller synthesis of a bidirectional inductive power interface for electric vehicles

Bidirectional Inductive Power Transfer (IPT) systems are preferred for Vehicle-to-Grid (V2G) applications. Typically, bidirectional IPT systems consist of high order resonant networks, and therefore, the control of bidirectional IPT systems has always been a difficulty. To date several different controllers have been reported, but these have been designed using steady-state models, which invariably, are incapable of providing an accurate insight into the dynamic behaviour of the system A dynamic state-space model of a bidirectional IPT system has been reported. However, currently this model has not been used to optimise the design of controllers. Therefore, this paper proposes an optimised controller based on the dynamic model. To verify the operation of the proposed controller simulated results of the optimised controller and simulated results of another controller are compared. Results indicate that the proposed controller is capable of accurately and stably controlling the power flow in a bidirectional IPT system.

A novel matrix converter based resonant dual active bridge for V2G applications

Dual-active bridges (DABs) can be used to deliver isolated and bidirectional power to electric vehicles (EVs) or to the grid in vehicle-to-grid (V2G) applications. However, such a system essentially requires a two-stage power conversion process, which significantly increases the power losses. Furthermore, the poor power factor associated with DAB converters further reduces the efficiency of such systems. This paper proposes a novel matrix converter based resonant DAB converter that requires only a single-stage power conversion process to facilitate isolated bi-directional power transfer between EVs and the grid. The proposed converter comprises a matrix converter based front end linked with an EV side full-bridge converter through a high frequency isolation transformer and a tuned LCL network. A mathematical model, which predicts the behavior of the proposed system, is presented to show that both the magnitude and direction of the power flow can be controlled through either relative phase angle or magnitude modulation of voltages produced by converters. Viability of the proposed concept is verified through simulations. The proposed matrix converter based DAB, with a single power conversion stage, is low in cost, and suites charging and discharging in single or multiple EVs or V2G applications.

Impact of PV units in LV feeders on distribution system reliability

Re-supplying loads on outage through cross-connect from adjacent feeders in a distribution system may cause voltage drop and hence require load shedding. However, the surplus PV generated in some of the LV feeders can prevent load shedding, and improve reliability. In order to measure these effects, this paper proposes the application of Direct Load Flow method[1] in reliability evaluation of distribution systems with PV units. As part of this study, seasonal impacts on load consumption together with surplus PV output power injection to higher voltage networks are also considered. New indices are proposed to measure yearly expected energy export, from LV to MV and from MV to higher voltage network.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.