From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea

Designing Innovative PV-powered applications for the urban environment:A design-driven multidisciplinary approach

The transport and residential sectors are two of the key areas where the transition to a fully renewable energy supply needs to take place in order to limit the emission of CO2 and other greenhouse gases. This thesis therefore explores how a multidisciplinary design-driven research approach can be used to develop technically functional, financially feasible and low-emissions PV-powered applications for these two sectors which are more likely to be adopted by end users. A feasibility model was first developed to simulate the operation of a grid-connected solar EV charging station with energy storage, showing that the longer an EV is driven the more affordable solar-powered charging becomes and a higher environmental dividend is achieved. A subsequent study for eight locations around the world indicates that with the right combination of battery and PV system sizes this charging system can be a feasible solution from a technical, financial and environmental perspective in comparison with both a gasoline-fuelled vehicle and a grid-charged EV. A conceptual design study resulted in the development of eleven innovative solar mobility applications, ranging from mobile EV charging stations to solar-powered bicycles and public transportation. Energy balance calculations for two sample locations show that the extent to which the PV electricity produced by these systems will meet vehicle demand will vary significantly depending on the type of application. Results from a user study aimed at evaluating the potential adoption of four existing solar-powered mobility applications found that despite having a mostly positive impression, respondents’ likelihood to adopt these applications in the near future was relatively low. However, a vast majority of respondents willing to pay more for an EV with integrated solar cells indicates that these applications are perceived as having an added value. Finally, the performance of home energy management system (HEMS) prototypes was evaluated using both simulation and user tests. This dual approach proved useful for quickly and accurately validating the operation of these products, but conflicting results during user tests highlight the complexity of user behaviour around household energy consumption and the importance of carefully designing HEMS to ensure they achieve their intended purpose

Development of Design Optimization for Smart Grid (DOfSG) Framework for Residential Energy Efficiency via Fuzzy Delphi Method (FDM) Approach

The smart grid revolution has benefited many sectors but the potential for design optimization among residential units has yet to be explored. Despite some researchers having negative perception of house design's association with the smart grid system, there is in fact potential for investigating design attribute optimisation aligned with the smart grid system. As electricity becomes a necessity of the 21st century society, residential dwellers are becoming more dependent on this indispensable source of energy. As such, this paper explains the development of a framework focusing on design optimization for residential units aligned to the smart grid system using the Fuzzy Delphi Method approach. It focuses on the significant smart grid components linked to the residential sector incorporating key design attributes for energy optimization purposes. The proposed framework denoted two main components of residential design optimization, depicted as indoor and outdoor parameters with its subsequent attributes further categorised into main and detailed components. Twelve design parameters were found to be substantial for the DOfSG development, intended to provide useful guide for aligning residential design towards the smart grid system in Malaysia

Energy Management of Distributed Generation Systems

The book contains 10 chapters, and it is divided into four sections. The first section includes three chapters, providing an overview of Energy Management of Distributed Systems. It outlines typical concepts, such as Demand-Side Management, Demand Response, Distributed, and Hierarchical Control for Smart Micro-Grids. The second section contains three chapters and presents different control algorithms, software architectures, and simulation tools dedicated to Energy Management Systems. In the third section, the importance and the role of energy storage technology in a Distribution System, describing and comparing different types of energy storage systems, is shown. The fourth section shows how to identify and address potential threats for a Home Energy Management System. Finally, the fifth section discusses about Economical Optimization of Operational Cost for Micro-Grids, pointing out the effect of renewable energy sources, active loads, and energy storage systems on economic operation

Energy Management Expert Assistant, a New Concept

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

供給と需要側を考慮した電源システムのモデリングと評価

Modelling and optimization of sustainable power system and energy network are becoming complex engineering. Demand side resources also need to be planned considering characteristics of district energy supply scenario. This research first analyzes the feasibility of VPP based on scenario of Chongming Island. VPP focuses on expansion of renewable energy and upgrade of efficient appliances, results verify the effectiveness of the VPP concept. Then investigates the techno-economic viability of high variable renewable integration. PV-PHS dispatch scenarious are carried out with constraints, PHS effectively recovers the suppression and decreases the PV power levelized cost. Introduction PV-PHS shifts merit order curve to right, decreasing power generating cost. Thirdly, cost and environmental benefits of optimal designed decentralized energy systems were investigated. Scheduled distributed energy resources could be optimized to benefit the public grid. Performance of dynamic price is investigated based on the social demonstration project experiment. Finally, the conclusions are provided.北九州市立大