Farhad Shahnia

Massive integration of distributed generators with inherently high intermittency and volatility leads to frequent voltage violations. Thus, an appropriate voltage/Var control (VVC) is essential for the secure and economic operation of distribution systems. The increasing penetrations of rooftop photovoltaic units strengthens the coupling between the medium and low voltage (MV-LV) distribution networks spatially, causes temporal interference of VVCs with each other on different timescales, and worsens the network unbalance profile especially on the LV sides. To address this challenge, this study proposes a spatial-temporal coordinated VVC for MV-LV unbalanced distribution networks. Based on 'decomposition-coordination' principle, the developed strategy consists of three modules of real-time evaluation of reactive capability of LV feeders, long-short-time coordinated VVC of MV network, and parallel short-time VVC of LV feeders. Based on the proposed strategy, the VVC optimization problems for these modules are formulated and jointly solved by mixed-integer second-order cone programming and linear programming methods, to minimize the voltage deviations within the MV-LV unbalanced distribution systems in real time. Finally, based on the joint simulation platform of MATLAB and Python, the effectiveness and superiority of the proposal are numerically verified on a real Australian distribution system.

The goals of this study are to ensure simultaneously a good control of energy injection into the network and power quality enhancement. Firstly, the injection mode of different levels of active power via the solar inverter is achieved through intelligent artificial controller. Then, to obtain a sinusoidal grid current waveform, it is proposed to compensate unwanted lower current harmonics caused by the nonlinear load connected at the NCP (Network Coupling Point) by active filtering strategy. Note that, the harmonics produced by the VSI (Voltage Source Inverter) converter due to its switching action are suppressed by a low-pass passive damping LCL-filter. The obtained network current is well filtered, it has a sine waveform and therefore its calculated THD is less than 5% that is verified the IEEE Std 5192014, recommended harmonic limits for current distortion in electrical systems.

The integration of distributed energy resources into power grids fosters the development of precise monitoring, protection, and control applications by employing immense spatiotemporal data from micro phasor measurement units ( \mu PMUs). For enhanced situational awareness, a comprehensive methodology is required for real-time synchro phasor forensic analysis using advanced machine-learning techniques to detect and classify anomalies in grid events. This paper presents a twostage analytical framework that combines waveletautoregressive integrated moving average (ARIMA)-based analysis with a machine learning approach to enhance the identification and classification of events by leveraging historical frequency and spectrum data. The raw data from the New England ISO and European Continental Split datasets were preprocessed in the initial phase, as they included multiple events. The process involves a Stationary Wavelet Transform (SWT) for denoising, and sliding window ARIMA models to identify the Rolling Standard Deviation (RSD) for feature extraction and threshold setting. The frequency excursions and oscillations are classified based on the Synchro phasor Event Detection Algorithm (SPEDA) as per statistical thresholds. The retrieved features of the detected and localized events were cross validated using machine learning classifiers in the next stage, enhancing the overall efficiency and effectiveness of the study. The study demonstrates that advanced computing facilities accelerate sophisticated calculations and reduce model training time.

This paper presents the transient stability enhancement of Doubly-Fed Induction Generator (DFIG)-based wind energy systems through the implementation of a Resistive Fault Current Limiter (RFCL). DFIG technology has gained popularity due to its ability to efficiently harness wind energy under variable conditions. However, its vulnerability to faults, particularly during symmetrical and asymmetrical disturbances, poses significant challenges to grid stability. This study investigates various internal and external control strategies, highlighting the limitations of conventional methods. The RFCL is proposed as an effective solution to mitigate fault impacts and improve system resilience. Simulation results demonstrate that integrating the RFCL significantly enhances transient stability, outperforming traditional fault current limiting approaches. This research contributes to optimizing DFIG performance and ensuring reliable wind energy generation.

An energy study of the Carnarvon power system using load demand, irradiance and photovoltaic generation data acquired from the power system operator, for calender year 2018, has been performed. This study utilised the HOMER Pro simulation system to determine photovoltaic and battery capacities to be applied to the Carnarvon power system to operate, without fuelled prime mover driven synchronous generation (FSG) input, over a 24 hour cycle given adequate irradiation. This goal is realised by using a metric that balances competing mechanisms in deciding PV and battery storage capacities. The metric calculation resulted in capacities of 35MW for PV and 90MWhr for battery storage, resulting in a renewable energy fraction of 96.2 %, 589 hours of FSG operation and levelised cost of energy (LCoE) of 47.6c/kWhr. Power ratio and storage ratio give dimensionless quantities for PV and storage sizes relative to load demand. These values can estimate PV and battery capacities for similar systems in similar environments to achieve comparable outcomes. The determined capacity of 35MW of PV is assumed to be all roof-top installed. This is based on roof-top potential estimates, made in other work, of 70MW for Carnarvon.

Despite the large uptake of solar energy around the world, low efficiency of the photovoltaic (PV) panels is their main drawback. This paper presents a novel maximum power point tracking (MPPT) for PV panels based on the Poincare Map technique that aims at increasing the efficiency of the solar panels. Using this technique, not only will make the solar panels more affordable from efficiency perspective, but also the external maloperations cannot jeopardize the accuracy of the plan, as shown by the study. Moreover, the studies show that the reliability of the plan under the standard operation of the solar panel is more than 98.94 %, and is over 97.19 % under external disturbances.

This paper investigates energy management in smart microgrids by incorporating energy storage batteries to improve the operational cost efficiency and system reliability. The considered cost and reliability index are respectively the battery costs and the loss of load expectation. Since operational indices depend on location and costs on battery capacity, the main challenge is determining the optimal capacity and installation location for batteries. To achieve both objectives, the functions are consolidated into a single overarching objective function. This problem is addressed through a novel optimization algorithm known as the Symbiotic Organisms Search (SOS) algorithm. Unlike other heuristic algorithms, the SOS algorithm requires no specific tuning parameters, allowing for faster convergence. To verify its efficiency, the algorithm's results are compared with those of the widely recognized Genetic Algorithm. A sodium-sulfur battery is selected for this study due to its high-power density, efficiency, and long-life cycle. Renewable energy sources utilized in this study are in the form of wind turbines and photovoltaic cells. The proposed methodology is tested on the IEEE 33-bus system, with results confirming its practical feasibility.

This paper investigates the design of a hybrid renewable energy and diesel generator microgrid for a remote aboriginal community in Western Australia using two techno-economic design optimisation programs, HOMER and XENDEE. Each program approaches the design optimisation problem in a different way (evolutionary simulation algorithm vs linear programming) and this paper seeks to compare the results from the different approaches. The optimisation results from the two programs are very similar for the rural hybrid microgrid case study, although there are differences in how each program reached its conclusions. A key difference identified is how each program handles the time-sequential nature of the demand profile and renewable energy resource.

Electricity systems around the world are experiencing a radical transition as the consequence of replacing fossil fuels, used for electricity production, by sustainable and cleaner energies. The growing penetration of renewable energies requires smarter techniques capable of handling the uncertainties of these intermittent sources. Along with this change, traditionally centralised power systems are also converting into distributed self-sufficient systems, often referred to as microgrids, that can operate independently. This talk will focus on remote area microgrids as a hot research topic in Australia and Southeast Asia that have hundreds of remote and off-grid towns and communities, and islands. It is expected that remote area microgrids will strongly benefit these remote locations in the forthcoming years. This talk will briefly introduce the progress of research in this field around the world and Australia, and will also discuss some of the technical challenges associated with interconnection of neighbouring microgrids as a key step to improve their survivability in the course of unexpected imbalances between the demand and the available generation from intermittent renewable resources.

The battery energy storage systems (BESSs) can support the distribution feeders by load-shifting and voltage regulation. Their capabilities can be fully employed by properly placing them within the feeders. Existing strategies mostly employ injection-based sensitivity analysis to improve the computation speed; however, they cannot fully consider the discharging impacts of BESSs, and tend to yield less cost-effective solutions. This paper proposes a sequential placement strategy for BESS placement in unbalanced distribution feeders, and takes into account the impact of time-varying load and renewable profiles. The developed technique aims at minimizing the primary investment and operation/maintenance costs of BESSs whilst maximizing the savings of the network operator in terms of loss and peak demand reduction. The performance of the developed technique is validated by detailed numerical analyses on a real distribution network.