411 research outputs found

    Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

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    The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses

    Non-Intrusive Disaggregation of Advanced Metering Infrastructure Signals for Demand-Side Management

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    As intermittent renewable energy generation resources become more prevalent, innovative ways to manage the electric grid are sought. In the past, much of the grid balancing effort has been focused on the supply side or on demand-side management of large commercial or industrial electricity customers. Today, with the increase in enabling technologies such as Internet-connected appliances, home energy management systems, and advanced metering infrastructure (AMI) smart meters, residential demand-side management is also a possibility. For a utility to assess the potential capacity of residential demand-side flexibility, power data from controllable appliances from a large sample of houses is required. These data may be collected by installing time- and cost-intensive monitoring equipment at every site, or, alternatively, by disaggregating the signals communicated to the utility by AMI meters. In this study, non-intrusive load monitoring algorithms are used to disaggregate low-resolution real power signals from AMI smart meters. Disaggregation results using both supervised and unsupervised versions of a graph signal processing (GSP) -based algorithm are presented. The effects of varying key parameters in each GSP algorithm, including scaling factor, sequence, and classifier threshold are also presented, and limitations of the algorithm based on energy use patterns are discussed. FM values greater than 0.8 were achieved for the electric resistance water heater and electric vehicle charger using the unsupervised GSP algorithm. The disaggregated signals are then used to develop energy forecasting models for predicting the load of controllable appliances over a given demand response period. ARIMA, SVR, and LSTM forecasting methods were evaluated and compared to a baseline model developed using the mean hourly power draw values. The minimum MAAPE was achieved for the water heater, with an approximate range of 10 < MAAPE < 20. The total energy flexibility of each appliance and the associated uncertainty of the combined disaggregation and forecast are characterized to assess the feasibility of this approach for demand-side management applications. The framework presented in this study may be used to characterize the ability of signals to be disaggregated from a larger dataset of AMI data, based on the whole-house signal characteristics. This analysis can aid grid managers in assessing the viability of selected devices, such as the water heater, for demand response activities.Ph.D

    Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

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    This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.Comment: PhD thesi

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation &amp; Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    A novel dynamic maximum demand reduction controller of battery energy storage system for educational buildings in Malaysia

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    Maximum Demand (MD) management is essential to help businesses and electricity companies saves on electricity bills and operation cost. Among different MD reduction techniques, demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for various markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize complex prediction models and rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. Hence, this research work proposes a novel incremental DB-SOINN-R prediction model and a novel dynamic two-stage MD reduction controller. The incremental learning capability of the novel DB-SOINN-R allows the model to be deployed as soon as possible and improves its prediction accuracy as time progresses. The proposed DB-SOINN-R is compared with five models: feedforward neural network, deep neural network with long-short-term memory, support vector regression, ESOINN, and k-nearest neighbour (kNN) regression. They are tested on day-ahead and one-hour-ahead load predictions using two different datasets. The proposed DB-SOINN-R has the highest prediction accuracy among all models with incremental learning in both datasets. The novel dynamic two-stage maximum demand reduction controller of BESS incorporates one-hour-ahead load profiles to refine the threshold found based on day-ahead load profiles for preventing peak reduction failure, if necessary, with no rigid parameters required. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets. The real-world performance of the proposed two-stage MD reduction controller that includes the proposed DB-SOINN-R models is validated in a scaled-down experiment setup. Results show negligible differences of 0.5% in daily PDRP and MAPE between experimental and simulation results. Therefore, it fulfilled the aim of this research work, which is to develop a controller that is easy to implement, requires minimal historical data to begin operation and has a reliable MD reduction performance

    The Influence of Electric Vehicle Availability on Vehicle-to-Grid Provision within a Microgrid.

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    By 2030, the number of electric vehicles (EVs) on the road is expected to increase to 11 million in the UK, meaning that there will be an increase in electricity demand. A potential solution to help manage this increase in demand is to use a technology called vehicle-to-grid (V2G) which is essentially a connection post that allows a bidirectional flow of energy, which means that EVs can charge and discharge when connected. Through this technology, the electrical grid can make use of the energy already stored in the battery of the EV. This research aimed to understand the effects of EV availability on V2G technology within a microgrid and evaluated the feasibility of providing ancillary services. A predictive model, primarily trained on internal combustion engine vehicle (ICEV) trips, used the UK’s historical travel data to predict the location of EVs, achieving significant understanding of travel behaviour and EV availability. Split into two tasks—predicting start and end locations—this model utilised light gradient boosting machine (LightGBM) due to its superior performance. After fine-tuning, it yielded a weighted average F1 score of 0.900 and 0.902 for tasks 1 and 2, respectively. The model, when informed by new, real-world EV data, derived travel start and end locations, which was the fed into an optimisation model. This optimisation model use a mixed integer linear programming (MILP) approach to schedule EV battery usage at the household level and study various case studies involving V2G technology. Simulations factored in different photovoltaic (PV) penetration rates, energy tariffs, and peer-to-peer (P2P) pricing mechanisms within a microgrid. First, the technical and economic benefits of home batteries, smart charging (V1G), and Vehicle-to-home (V2H) systems in EVs were evaluated, with an emphasis on performance and electricity bill reduction. The second case studied the potential of EVs to provide short term operation reserve (STOR) services. The third case explored a payment mechanism to optimise the state of charge (SOC) for EVs under V1G and V2H technologies for a week and estimate the energy available for restoration services. The study reveals that both stationary home batteries and EVs, when integrated with solar power and dynamic tariffs, can effectively reduce electricity costs, despite the fluctuating availability of EVs. Notably, EVs, when combined with P2P energy sharing and V2H systems, offer comparable performance to stationary batteries, in addition to their transportation benefit. In terms of STOR provision, EVs meet the technical requirements, with their availability significantly influencing STOR provision. Factors like energy tariffs, solar power penetration rates, and P2P mechanisms have minimal effect on the STOR energy amount, but they do affect the overall microgrid performance. The study also highlights the need to maintain a 15% surplus of EVs within the microgrid for ensured resilience. Effective strategies to maintain a high SOC in EVs include higher payment rate systems, implementation of V1G and V2H strategies, and dynamic energy tariffs. The study, however, recommends limiting users to V1G to prioritise potential energy use for restoration services. Although EV availability affects the minimum SOC, it is not more significant than other factors such as EV penetration rates, energy tariffs, and P2P price mechanisms. The findings imply that EV availability can reduce some of the benefits that stationary home battery have, such as surplus noon charging, while V2H might match home batteries in certain situations. EVs can offer STOR services as the fulfil most of the technical requirements, but the energy amount is dependant on available EVs during STOR events. EV availability had minimal effect on maintaining minimum SOC for a week that could potentially be used for restoration services, with energy tariffs and end-of-week incentives being more influential. Different PV penetration rates, energy tariffs, and P2P price mechanisms each have varied impacts on grid performance and V2G provision depending on the scenario

    Power Quality Management and Classification for Smart Grid Application using Machine Learning

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    The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development

    Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

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    In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models
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