Smart household management systems with renewable generation to increase the operation profit of a microgrid

During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.fi=vertaisarvioitu|en=peerReviewed

Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv

Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions

The introduction of dynamic electricity pricing structures such as Time of Use (TOU) rates and Day Ahead Pricing (DAP) in residential markets has created the possibility for customers to reduce their electric bills by using energy storage systems for load shifting and/or peak load shaving. While there are numerous system designs and model formulations for minimizing electric bills under these rate structures the use of these systems has the potential to cause an increase in emissions from the electricity system. The Increase in emissions is linked to the difference in fuel mix of marginal generators throughout the day as well as inefficiencies associated with energy storage systems. In this work a multi-objective optimization model is designed to optimize reduction in cost of electricity as well as reduction in carbon dioxide (CO2) emissions from the electricity used by residential customers operating a battery energy storage system under dynamic pricing structures. A total of 22 different regions in the US are analyzed. Excluding emissions from the model resulted in an annual increase of CO2 emissions in all but one region ranging from 60-2000kg per household. The multi-objective model could be used to economically reduce these additional emissions in most regions by anywhere from 5 – 1300kg of CO2 per year depending on the region. When using the multi-objective model several regions had a net decrease in CO2 emissions compared to not using a battery system but most had a net increase

Demand side management of electric water heater with a photovoltaic system

Master's thesis Renewable Energy ENE500 - University of Agder 2018This thesis presents the development of a load controller, which can be used to store self- produced electricity as hot water in a regular electrical water tank. The electricity market today facing challenges towards grid utilisation and consumption during peak hours. To prevent massive investment for the grid operators for upgrading the grid to handle peak hours, a better utilisation is necessary. An expected increase in electricity prices together with a reduction of cost for renewable energy production opens for new solutions to reduce the grid electricity consumption. The problem statement was to design a controller for implementation on a regular electric water heater in a household, with the aim to reduce the grid peaks, and the price of hot water in the household, by increasing the temperature when there was excess electricity available from the photovoltaic sys- tem. By using an already existing household appliance, the investment cost of the overall system compared to, i.e. batteries are low. Previous research has focused towards reducing grid consump- tion during peak hours from a grid operators point of view, and the approach was to use models to predict the water consumption and remotely control the heater. However, recent research has included demand-side management and more precise models. In the thesis, the controller is used for demand-side management, and only require a few modi cations to the electric water heater. For estimating water consumption, two pro les are used which is based on average and electricity based water consumption. The controller uses logic and forecast data to set the tank temperature according to expected PV production. During testing, some improvements were made to optimise the controller, but for further increasing the energy storage and cost reduction, additional improve- ments are necessary. The results show that energy storage would reduce the grid consumption, and reduce the price of electricity between 9-55% per day with grid tari , depending on the amount of self-produced electricity. However, more experiments and with other water usages is required to con rm the saving potential for grid consumption. Besides, the consumption in high demand periods is reduced by shift the demand from the Electric Water Heater(EWH). This can be further improved by power control of the heating element in the EWH as the results show that without power control, the savings results are inconclusive. Overall, the controller reduces the peaks in the grid and lowers the cost of hot water, but the exact saving is not possible to predict until the new electricity prices structures are available. Nevertheless, the results show that there is possible to store large amounts of energy as hot water, without a ecting the user-comfort, with the bene ts of reducing grid consumption and peaks

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

中国陝西省南部における伝統的な住宅の冬の省エネルギー設計に関する研究

The combination of solar energy and buildings can greatly save energy, and a great deal of practical and theoretical research has been conducted on solar buildings around the world. Rural areas in southern Shaanxi, China, have wet and cold winters. The average room temperature is 4°C and below 2°C at night, which greatly exceeds the range of thermal comfort that the human body can tolerate. In response to a series of problems such as backward heating methods and low heating efficiency in southern Shaanxi, two fully passive heating methods are proposed for traditional houses in the region. They are rooftop solar heating storage systems and thermal storage wall heating systems (TSWHS), respectively. These two systems have been compared with the status quo heating system to confirm the practicality of the new system and to provide an idea for heating and energy saving in traditional houses in rural areas.北九州市立大

Optimal Battery Management with ADHDP in Smart Home Environments

In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method