• Energy Research

With today's growth of prosumers and renewable energy resources, it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation. In this context, demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies. However, effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders. This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications, assisted by an improved event-based non-intrusive load monitoring approach. For the said purposes, an improved event detection algorithm and machine learning model: support vector machine with a combination of genetic algorithm and GridSearchCV, is presented. This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario. In the given context, all the simulations are carried out on low sampling real-world load measurements: Pecan Street-Dataport, where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes. Based on the presented case study and analysis of the results, it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification. Moreover, it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

One of the key techniques toward energy efficiency and conservation is nonintrusive load monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window (SW) that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis, it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision, respectively.

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.

Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.