I-COSTE 2024 – Page 6 – NPS Australia Submission System

Can urban retrofitting achieve a positive energy balance? A case Study of four European Positive Energy District

Abstract— Urban retrofitting has emerged as a key strategy in the transition towards sustainable cities, with Positive Energy Districts (PEDs) serving as a model for achieving energy-positive urban environments. This paper explores the potential for urban retrofitting to achieve a positive energy balance through a case study of four existing districts in European Municipalities: Settimo Torinese (Italy), Großschönau (Austria), Amsterdam (Netherlands), and Resita (Romania). The analysis leverages energy balance simulations, considering various retrofitting scenarios, including building insulation, photovoltaic (PV) installations, and the adoption of flexible grid usage. The findings indicate that while achieving a PED is challenging, it is attainable through a combination of aggressive retrofitting measures, renewable energy integration, and smart energy management. The study highlights the importance of context-specific strategies, as climatic and urban characteristics significantly influence the outcomes. It aims to add to the ongoing discourse on sustainable urban development by providing empirical insights into the pathways and challenges of achieving PEDs through urban retrofitting.
Keywords — Positive Energy Districts, PED, retrofitting, Climate-Neutral Districts

A Study on Reactive Power Control Using Variable Gain and Voltage Limiter for Grid-Forming Converters

In our previous study, an active power control method called variable gain control (VGC) has been proposed for grid-forming (GFM) converters. In the present paper, a reactive power control method for GFM converters to prevent an overcurrent and an alleviate a voltage oscillation under a three-line-to-ground (3LG) fault. In the proposed method, the magnitude of the voltage output of GFM converters is varied in accordance with the terminal voltage. In addition, a novel idea of variable gain and a voltage limiter is applied to avoid overcurrent. Numerical simulations demonstrate the effectiveness of the proposed method in suppressing overcurrent and voltage variation during 3LG faults.

A Market, Economic, and Technical Analysis of a Community Solar Electricity Aggregator Approach for Local Governments to meet their Net Zero Emissions Energy Needs

This research can enhance the level of understanding of local governments in Western Australia on possible approaches to meet their net zero emissions targets.

Energy Consumption Forecasting Using Ensemble Machine Learning Models in Smart Grid

Energy Consumption Forecasting Using Ensemble Machine Learning Models in Smart GridAccurately predicting medical charges is crucial for healthcare providers, insurance companies, and policymakers to manage costs and allocate resources efficiently. This study conducts a comparative analysis of five machine learning algorithms—Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor, and Gradient Boosting Regressor—to evaluate their performance in predicting medical insurance charges. Utilizing a dataset of patient demographics, health indicators, and lifestyle factors, we identify the key variables that most significantly influence medical expenses. Our findings reveal that certain algorithms outperform others in predictive accuracy, with XGBoost Regressor showing the highest accuracy (R² = 0.94). Additionally, the study highlights the most critical factors contributing to medical charges, are Smoking status, BMI, and Age. The analysis of feature importance across different models provides valuable insights into the underlying drivers of healthcare costs. This research contributes to the growing body of literature on healthcare analytics by offering a dual focus on predictive modeling and variable importance. The results underscore the potential of machine learning to enhance decision-making in the healthcare industry, particularly in optimizing resource allocation and cost management.

North Atlantic Offshore Wind Characteristics: Modeling and Comparison with Field Measurements and Industry Standards

Wind characteristics are critical to offshore wind resource development. The power output from a wind turbine is very sensitive to the local wind speed. Wind speed measurement is often limited to surface area close to Lidar buoys or meteorological stations and up to 200m due to the range of remote sensing devices. On the other hand, wind fields from ground level and up to 20000m above ground level can be simulated using Weather Research & Forecasting (WRF) model. In this study, WRF simulations are performed for the North Atlantic offshore waters to obtain wind speed time and spatial properties. Statistics of wind speeds for selected sites are derived and validated with field measurements. Wind vertical profiles are compared with ISO and IEC standards, and a power law profile is further derived to find the best fit. It is also demonstrated that the WRF model is reliable to forecast wind data, optimize prediction and improve reliability for coastal and offshore energy development. The wind modeling and characterizing can be extended to global regions to identify prospects with the most renewable energy potentials.

Retrofitting Legacy CNC Machines with a Focus on Energy Consumption

We are outlining the detailed process of retrofitting a legacy CNC machine to add connectivity and integrate it into the Industry 4.0 framework. This work offers significant benefits, especially for small and medium-sized companies. The outcome has the potential to enhance sustainability in industrial processes by focusing on the machine’s energy consumption.

A Digital Twin Framework in Manufacturing Systems with a Focus on Energy Consumption

We have developed a framework for integrating digital twins into older CNC machines, focusing on monitoring their electrical energy consumption. The digital twin’s virtual component utilizes discrete event simulation software in a digital manufacturing environment. Our goal with this proposal is to promote greater sustainability in manufacturing systems.

ResiPlant-5: A CNN model for disease detection in citrus fruits and leaves

This paper introduces ResiPlant-5, a 5-layer con-
volutional neural network (CNN) designed for precise plant
disease diagnosis. Sequential CNN model suffer form vanishing
gradient problem. To overcome vanishing gradients and im-
prove deep model learning, the design uses skip connections,
inspired by Residual Networks (ResNets). Skip connections
provide connections between non-adjacent layers, improving
gradient propagation and feature retention. This approach lets
the model maintain important properties from previous layers
while training deeper networks without sacrificing speed. Using
deep learning and residual connections, ResiPlant-5 successfully
addresses difficult image classification challenges, hence making
it feasible to identify the disease in the plants. The model has been
trained and tested using two publicly available datasets. The first
dataset is the citrus dataset, which contains images of citrus leaves
and citrus fruit. The second dataset is the sweet orange dataset.
The results indicate that the proposed model demonstrates an
approximate increase in accuracy of 2%, 6%, and 8% on the
Sweet Orange, citrus leaves, and citrus fruit datasets, respectively,
compared to the VGG16, VGG19, and ResNet50 models

PMU-Based Short Circuit Capacity Estimation using System and Load Impedance Variation Ratio

This paper presents a new method for the estimation of short-circuit capacity using PMU (Phasor Measurement Units) measurements of voltage and current phasors. The proposed method has two functions: the improvement of the estimation accuracy by selecting only PMU measurements which are suitable for the estimation, and the determination of whether the estimation values are erroneous or not. The proposed method focuses on variations in system side Thevenin equivalent (TE) impedance and load impedance, which affect the estimation accuracy. First, it analyses how the changes in each component of TE or load impedance affect the short-circuit estimation. Based on these results, the proposed method is developed. Then, the proposed method is validated by numerical simulations, and it is confirmed that the proposed method can properly distinguish between the correct and incorrect estimates and obtain more accurate estimation values of short-circuit capacity than other existing methods.

A Phased Training Method for Stabilizing the Training Process of Power Grid Voltage Control Agents with Deep Reinforcement Learning

In recent years, renewable energy sources such as photovoltaic power generation system (PV) have been rapidly integrated into many power grids around the world. The higher penetration of renewable energy resources has made more difficult to maintain proper voltage using conventional method of Load Ratio Transformer (LRT) tap-changing in view of rapid generation fluctuation caused by weather condition change. To solve this problem, the reactive power control with power conditioning system (PCS) of PV can be used as a voltage regulation resource. Recent works have developed multi-timescale voltage control with short-term control by PCS and long-term control by LRT using deep reinforcement learning (DRL).
Most of these methods achieve coordination between agents in different control cycles by reward calculation. However, the training becomes unstable due to the improper management of change of each agent’s strategy, and it may not be possible to control voltage of power grid.
In this paper, the authors have proposed a phased training method to improve the stability of the training process for each agent that performs either LRT control or PCS control in power grid voltage control with DRL. The effectiveness of the proposed method is verified by numerical simulations using a power grid model with large PVs.

Conference Site: I-COSTE 2024