To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital

Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting

State of the art of machine learning models in energy systems: A systematic review

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation

With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency

Data Science in Energy and Environmental Systems With Multiple Data Sources

The energy sector in modern society is undergoing a rapid transformation from fuel-based generation to renewable generation. Further, distributed supply and demand, grid-responsive demand management, and other complex technology increasingly rely on environmental data throughout the energy supply chain, from power production to end uses. However, data analysis in energy systems presents major technical challenges, including spatio-temporal heterogeneity, localized characteristics, and disparate data sources. This dissertation study aims to design data science solutions that address some of these challenges and model the dynamics of ambient environmental conditions that are closely tied with energy system operations. Climate conditions are often temporally and spatially correlated and exhibit a non-stationary nature, constantly changing all the time. To fully characterize these characteristics, this study utilizes the rich data available from multiple sources, including data collected at spatially distributed locations and data generated from disparate sources, e.g., field meteorological data and physics-based numerical weather prediction data. This dissertation initiates two major ideas: (a) making use of data collected from multiple spatially dispersed locations; and (b) integrating data generated from physics-based numerical weather prediction models with actual climate measurements through a linkage function. Specifically, the following three research topics are investigated. The first study develops a probabilistic model for assessing wind resources at a target location by utilizing wind data collected at nearby meteorological stations. By quantifying daily and spatially correlated diurnal patterns of the wind speed at multiple locations, the developed integrative approach provides compelling capabilities for evaluating the wind variability at non-observational locations, while quantifying prediction uncertainties. The results will provide rich information for deciding suitable wind farm sites. Next, we make use of temperature projections from physics-based global climate models for the purpose of long-term electricity load predictions. While the physics-based climate models can provide useful climate projections in the long run, they inevitably exhibit systematic discrepancies (also called `bias'), compared to actual climate conditions, because of incomplete characterization of local or regional variations. We calibrate the climate model projections to address possible biases and provide a long-term density prediction of peak electricity load. The results provide useful insights on how the daily peak demand densities would change over time, in response to climate change and other socio-economic factors. Finally, we present another bias correction model that quantifies the spatially and temporally correlated bias from the physics-based numerical model output for urban temperature modeling. By combining both types of data, our approach can successfully characterize localized environmental variations over space and time and greatly improve the prediction accuracy compared to that of the original physics-based numerical model. hl{The proposed approach helps understanding temperature variations over dispersed locations, depending on urbanization intensity. Such results can be useful for predicting electricity demand and effectively managing power system operations such as demand response programs.} The advantages of all proposed approaches are demonstrated with case studies using actual data. The results validate that the proposed approaches successfully address some of the challenges discussed above that arise in energy and environmental systems.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169899/1/mapsossa_1.pd

Artificial intelligence for decision making in energy demand-side response

This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process