A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Enhanced MPPT Controllers for Smart Grid Applications

Over the past years, the energy demand has been steadily growing and so methods of how to cope with this staggering increase are being researched and utilized. One method of injecting more energy to the grid is renewable energy, which has become in recent years an integral part of any country’s power generation plan. Thus, it is a necessity to enhance renewable energy resources and maximize their grid utilization, so that these resources can step up and reduce the over dependency of global energy production on depleting energy resources. This thesis focuses on solar power and effective means to enhance its efficiency through the use of different controllers. In this regard, substantial research efforts have been done. However, due to the current market and technological development, more options are made available that are able to boast the efficiency and utilization of renewables in the power mix. In this thesis, an enhanced maximum power point tracking (MPPT) controller has been designed as part of a Photovoltaic (PV) system to generate maximum power to satisfy load demand. The PV system is designed and simulated using MATLAB (consisting of a solar panel array, MPPT controller, boost converter, and a resistive load). The solar panel chosen for the array is Sun Power SPR- 440NE-WHT-D and the array is designed to produce 150 kW of power. The MPPT controller is designed using three different algorithms and the results are compared to identify each controller’s fortes and drawbacks. The three designed controllers used are based on Perturb and Observe (P&O) algorithm, Incremental Conductance (INC) with an Integral Regulator (IR) and Fuzzy Logic Control (FLC). Each controller was tested under two different scenarios; the first is when the panel array is subjected to constant amount of solar irradiance along with a constant atmospheric temperature and the second scenario has varying solar irradiance and atmospheric temperature. The performance of these controllers is analyzed and compared in terms of the output power efficiency, system dynamic response and finally the oscillations behavior. After analyzing the results, it is shown that Fuzzy Logic Controller design performed better compared to the other controllers as it had in most cases the highest mean power efficiency and fastest response

A Case Study on Application of Fuzzy Logic based Controller for Peak Load Shaving in a Typical Household\u27s Per Day Electricity Consumption

The cost of electricity for consumers depends on the cost of generation, transmission, and distribution of power. The electrical load consumed by consumers per day is not constant throughout the day. The utilities must be capable of meeting the load demand, which means they must have enough electricity generation potential and necessary infrastructure. This cost is significant. However, the revenue they generate will only be for the actual use of electricity by the consumers. In general, the electrical power generation is done in stages, always generating a base load. As demand changes throughout the day, additional stages of power generation are brought online to meet the changes in demand. This approach of management is known as supply-side management. Theoretically, if it is possible to manage the load such that there is lower peak demand and the difference between peak load and base load were minimized, the generation capability and grid infrastructure required to provide reliable power would be reduced resulting in lower costs for utility companies and ultimately consumers. This management strategy is referred to as demand-side management or demand response. In this research, a small-scale smart grid is modeled in Simulink to mimic the electrical grid. A Smart controller based on fuzzy logic is developed to control charging and discharging of an electric vehicle battery to provide extra power during peak times and to act as load (storing energy) during off-peak time to provide a more manageable and balanced load as seen by the grid. A comparative study is presented of electricity consumption throughout the day with or without the smart controller. The results show the significant reduction in peak demand, much smoother load curve for the grid, and a decrease in per kilowatt cost of electricity for the given day when newer pricing structures are applied

Modeling and Simulation of Protective Relay for Short Circuits in AC Micro-grids using Fuzzy Logic

The duo of high human appetite for electricity in the 21st century and high human population growth rate entail inadequacy of contemporary electric power protective systems for the emerging micro-grid. This thesis presents results of a research which seeks to propose a new model of protective device for short circuits in ac micro-grids. Response of the proposed relay is consistent with a reliable device. Consequently, a protective relay for short circuits in micro-grids is proposed

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Power Quality Enhancement in Electricity Grids with Wind Energy Using Multicell Converters and Energy Storage

In recent years, the wind power industry is experiencing a rapid growth and more wind farms with larger size wind turbines are being connected to the power system. While this contributes to the overall security of electricity supply, large-scale deployment of wind energy into the grid also presents many technical challenges. Most of these challenges are one way or another, related to the variability and intermittent nature of wind and affect the power quality of the distribution grid. Power quality relates to factors that cause variations in the voltage level and frequency as well as distortion in the voltage and current waveforms due to wind variability which produces both harmonics and inter-harmonics. The main motivation behind work is to propose a new topology of the static AC/DC/AC multicell converter to improve the power quality in grid-connected wind energy conversion systems. Serial switching cells have the ability to achieve a high power with lower-size components and improve the voltage waveforms at the input and output of the converter by increasing the number of cells. Furthermore, a battery energy storage system is included and a power management strategy is designed to ensure the continuity of power supply and consequently the autonomy of the proposed system. The simulation results are presented for a 149.2 kW wind turbine induction generator system and the results obtained demonstrate the reduced harmonics, improved transient response, and reference tracking of the voltage output of the wind energy conversion system.Peer reviewedFinal Accepted Versio

Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation