Hassan Al-Saadi

@adelaide.edu.au

The University of Adelaide

Hassan Al-Saadi


               Download Compact PDF  
https://researchid.co/hassan2lsaadi85
Hassan Al-Saadi received the BSc & MSc in Electrical Power Engineering, then he received his PhD in Electrical & Electronics Engineering from The University of Adelaide, South Australia, Australia in Dec. 2018. After PhD, Hassan worked as a visiting researcher to further his research in conduct/analyze/synthesize/developing new methods and strategies for energy/cost optimization. I am now specialist in risk analysis using machine learning (classification, regression, clustering), power system analysis (power flow, redial power flow), applied mathematics (statistical sciences, probability and decision theory, operational analysis, optimization theory, numerical analysis, scientific computation), uncertainty analysis (probabilistic and possibilistic assessment), Modeling and Simulation, computational techniques in Science, computer graphics and visualization.

EDUCATION

PhD in Electrical & Electronics Engineering from The University of Adelaide
Thesis by Publications titled "Probabilistic hosting capacity and risk analysis for distribution networks"

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering

FUTURE PROJECTS

Community Battery optimization

A community battery is a shared, medium-scale energy-storage system (typically 50 kWh – 5 MWh) that stores electricity—usually from local rooftop solar—for use by multiple households or a neighbourhood instead of a single customer.


Applications Invited
8

Scopus Publications

Scopus Publications

  • New risk analysis considering spatial correlations among connected PVs for bidirectional distribution feeders
    Hassan Al‐Saadi, Rastko Zivanovic, Said Al‐Sarawi
    International Transactions on Electrical Energy Systems, 2020
    The distribution networks are in a transition stage from being “passive” (consuming energy and typically with unidirectional power flows) into “active” (consuming/producing energy with bidirectional power flows). This transition has exposed the networks to stochastically behaving risks such as violation of the prescribed limits of power quality or overloading the network elements. The current paper presents a new risk analysis to quantify the risks of violating operational constraints due to a large number of small-size Photovoltaics (PVs). Risk's likelihood and severity are estimated based on the relative frequency of the number and the relative frequency of the accumulative depth of a violation, respectively. This article has two objectives. The first is to determine the hosting capacity of the targeted network. The second is to address the effect of spatial correlation on risk quantification, specifically the effect of perfectly positive correlation (PEC) to the effect of partially positive correlation (PAC). Modelling PAC through spatial rank correlation is established utilizing the Nataf transformation due to the non-Gaussianity of the probability densities of PV uncertainties. The approach is implemented on two distribution networks: IEEE 13 bus test distribution feeder and a large distribution network with multiple zones situated in South Australia. The approach is location-specific and time-varying. Hosting capacity is determined with the results of PEC showed an overestimation of the problem in comparison with the more realistic simulation under PAC. The analysis outcomes can help distribution network operators in managing and regulating the growing risks of high PV penetration.
  • Hosting capacity determination via risk analysis approach involving likelihood and severity of operational constraint violation
    Hassan Al-Saadi
    Hosting Capacity for Smart Power Grids, 2020
  • Assessing Algorithms of Phasor Measurements Optimal Placement for State Estimation
    Hatim G. Abood, Hassan Al-Saadi, Ghassan Abdullah Salman
    2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies Csudet 2019, 2019
    Power System State estimation utilizes the available measurements to estimate the state of the system, which is necessary for maintaining energy service and for the applications of smart grids. Hence, obtaining an accurate and stable state estimator is the main objective to most of the meter placement studies that are related to the state estimation. However, modern power systems have recently started employing advanced meters such as the Smart meters and Phasor Measurement Units (PMUs). However, a clear majority of the available PMUs placement algorithm discards the quality and numerical stability of the state estimation solution and focus only on achieving a completely observable power system. This leads to creating non-realistic meter configuration since the power systems have already their own measurement set. Therefore, the optimal placement algorithms should concern the existing configuration of the power network before suggesting new schemes. This paper investigates the validity of the existing optimal placement problem and their response to the requirements of the state estimation solution and provides suggestion to the practical placement strategies. The paper compares 25 of the top-cited papers in the research area of PMU optimal placement and assess the objectives of these papers in terms of the power system state estimation.
  • Hourly-assessment of grid hosting capacity for active distribution network
    Hassan Al-Saadi, Said Al-Sarawi, Rastko Zivanovic, Hatim G. Abood
    2018 International Conference on Probabilistic Methods Applied to Power Systems Pmaps 2018 Proceedings, 2018
    To support increasing penetration of small PV sources within existing distribution networks, there is an increasing interest in providing supporting, monitoring and controlling infrastructure to transform from passive to Active Distribution Networks (ADN). In order to operate ADN in an optimal way, the Hosting Capacity (HC) measure related to the maximum feasible capacity of PV sources should be estimated on an hourly basis. In this paper, HC has been estimated by conducting stochastic simulations in which the uncertainties of the solar irradiance and load variation are considered. The spatial correlations among PVs and loads have been taken into account by utilizing the N ataf transformation. Two performance indices based on technical violations (overvoltage and overloading) are established in order to depict the deterioration in the operational performance as the number of PV connections increase. Both the number and severity of a technical violation for each hour are considered. The relative frequency distribution is used to compute the probabilistic expectation and percentiles for each violation. The method is tested on large distribution network with several feeders and sub-feeders located in South Australia.
  • Probabilistic analysis of maximum allowable PV connections across bidirectional feeders within a distribution network
    Hassan Al-Saadi, Rastko Zivanovic, Said F. Al-Sarawi
    2017 Asian Conference on Energy Power and Transportation Electrification Acept 2017, 2017
    The paper presents a probabilistic approach (PA) to quantify the impacts of increased PV connections in bidirectional feeders within a distribution network. The aim is to establish a tool that can serve distribution network operators (DNOs) in seizing the maximum allowable photovoltaic (PV) connections, and ultimately with their responsibility on providing a reliable and secure power. An uncertainty model based on clearness index is utilized to predict the actual PV power injected into the utility following the Australian meteorological conditions. Three assessment indices are established and assessed using the Quasi Monte Carlo method. A large distribution network situated in South Australia is currently under test where six zones are chosen to have potentials of being bidirectional. The results presented in this paper show that the uncertainty behaviors of PVs differ from a feeder to another and can be quantified to seize the maximum PV allowance.
  • Uncertainty Model for Total Solar Irradiance Estimation on Australian Rooftops
    Hassan Al-Saadi, Rastko Zivanovic, Said Al-Sarawi
    E3s Web of Conferences, 2017
    The installations of solar panels on Australian rooftops have been in rise for the last few years, especially in the urban areas. This motivates academic researchers, distribution network operators and engineers to accurately address the level of uncertainty resulting from grid-connected solar panels. The main source of uncertainty is the intermittent nature of radiation, therefore, this paper presents a new model to estimate the total radiation incident on a tilted solar panel. Where a probability distribution factorizes clearness index, the model is driven upon clearness index with special attention being paid for Australia with the utilization of best-fit-correlation for diffuse fraction. The assessment of the model validity is achieved with the adoption of four goodness-of-fit techniques. In addition, the Quasi Monte Carlo and sparse grid methods are used as sampling and uncertainty computation tools, respectively. High resolution data resolution of solar irradiations for Adelaide city were used for this assessment, with an outcome indicating a satisfactory agreement between actual data variation and model.
  • Solution methods of Ill-conditioned power system state estimation: A comparative study
    Hatim G. Abood, Victor Sreeram, Hassan Al-Saadi, Yateendra Mishra
    Tensymp 2017 IEEE International Symposium on Technologies for Smart Cities, 2017
    Power system state estimation is based on an iterative process for solving the weighted least squares (WLS) algorithm via the so-called Normal Equations (NE). This process is prone to be numerically unstable if the power system is ill-conditioned. Several reasons contribute to creating an ill-conditioned state estimator. However, this paper focuses on the effect of the high R/X ratios. A review of the main approaches that have been used for avoiding or mitigating this problem of ill-conditioning in the state estimation is presented in this paper. Additionally, simulation tests using MATLAB are implemented on 5-Bus and IEEE 30-Bus systems for evaluating these methods' performance according to their condition number and other characteristics of each process.
  • Probabilistic Hosting Capacity for Active Distribution Networks
    Hassan Al-Saadi, Rastko Zivanovic, Said F. Al-Sarawi
    IEEE Transactions on Industrial Informatics, 2017
    The increased connection of distributed generation (DG), such as photovoltaic (PV) and wind turbine (WT), has shifted the current distribution networks from being passive (consuming energy) into active (consuming/producing energy). However, there is still no consensus about how to determine the maximum amount of DGs that are allowed to be connected, i.e., how to quantify a so-called “hosting capacity” (HC). Therefore, this paper proposes a novel risk assessment tool for estimating network HC by considering uncertainties associated with PV, WT, and loads. This evaluation is performed using the likelihood approximation approach. The paper, also, proposes a utilization of clearness index for localized solar irradiance prediction of PV. In addition, we propose the use of sparse grid technique as an effective means for uncertainty computation while the use of Monte Carlo technique is taken for a comparison purpose. Two actual distribution networks (11-buses and South Australian large feeder) are considered as case studies to demonstrate the usefulness of the proposed tool.