Design and Implementation of a Computer-Based Power Management System

Power supply is of great importance and interrupting its supply may impact negatively on our daily activities. With the application of power management systems, the vulnerability of energy is effectively managed, thereby ensuring a smooth flow of energy requirements for domestic and industrial operations. This also reconciles efficiency, safety, economic, health and environmental conditions. Therefore, this paper presents the design, construction and implementation of a computer-based power management system for household applications. It enables the user to transfer management of supply to appliances in the house to a real time monitoring, switching and control system. This is achieved by programming an Atmega 328 microcontroller, which coordinates the overall activities of the system from a central control unit through an ESP8266 module. This wireless fidelity (Wi-Fi) module is where internally processed result is being sent to the central control unit. System design shows that the interoperability of the power management system is hinged upon a Wi-Fi as the signals are sent as packets in ASCII format from the point harnessed by the GUI software. The design was tested for performance and results show that when the power up icon is clicked on the personal computer, the bulb glows and when the power down button is clicked, the bulb goes off. Also, the measured and actual current of the transformer used in the design, justifies the efficiency of the power management system. The system design was seen to be more scalable and flexible when compared with existing home automation systems, and the hardware interface module can be handled by one server when there is Wi-Fi coverage. In conclusion, it is seen that power can be effectively managed from the personal computer, thereby reducing the overall power consumption in the facility

Design and Implementation of Wireless Smart Home Energy Management System Using Rule-Based Controller

Most residential units still rely on conventional energy supplied by utilities despite the continuous growth of renewable energy resources, such as solar and wind energy systems in power distribution networks. Utilities often use time-of-use energy pricing, which increases the interest of energy consumers, such as those in commercial and residential buildings, in reducing their energy usage. Thus, this work demonstrates the design and implementation of a home energy management (HEM) system that can automatically control home appliances to reduce daily energy and electricity bill. The system consists of multiple smart sockets that can read the power consumption of an attached appliance and actuate its on/off commands. It also consists of several other supporting instruments that provide information to the main controller. The smart sockets and supporting instruments in the system wirelessly provide the necessary data to a central controller. Then, the system analyzes the data gathered from these devices to generate control commands that operate the devices attached to the smart sockets. Control actions rely on a developed online rule-based HEM scheme. The rules of the algorithm are designed such that the lifestyle of the user is preserved while the energy consumption and daily energy cost of the controlled appliances are reduced. Experimental results show that the central controller can effectively receive data and control multiple devices from up to 18 m away without loss of data on the basis of a scheduled user program code. Moreover, online adaptation of the HEM scheme confirms significant reductions in the total daily energy consumption and daily electricity bill of 23.5 kWh and $2.898, respectively. Therefore, the proposed HEM system can be remarkably useful for home owners with high daily energy consumption

Control System for V2H (Vehicle To Home) Systems

Systémy Vehicle To Home využívají energii z baterie elektromobilu k napájení chytrého domu a přebytečnou energii ukládají zpět do baterie elektrického vozidla. Náplní této diplomové práce je návrh a realizace řídicí jednotky pro správu energetických toků v systémech V2H. Zvoleným protokolem pro nabíjení/vybíjení elektromobilu je standard CHAdeMO, který umožňuje komunikaci po sběrnici CAN. Navržená řídicí jednotka s názvem Energy Flow Control Unit (EFCU) se skládá z jednodeskového počítače Raspberry Pi 3B+, navržené desky plošných spojů a elektroměru s komunikací po protokolu Modbus. Řídicí jednotka propojuje nadřazený systém chytrého domu, systém správy baterie elektromobilu BMS a obousměrný měnič (umožňující připojení elektrického vozidla do energetické infrastruktury chytrého domu).Vehicle To Home systems use energy from electric vehicle battery to supply the smart home and store redundant energy back into the electric vehicle’s battery. The content of this diploma thesis is the design and implementation of a control unit for energy flow management in V2H systems. The selected protocol for charging/discharging an electric vehicle is the CHAdeMO standard, which enables communication via the CAN bus. The designed control unit called Energy Flow Control Unit (EFCU) consists of a single-board computer Raspberry Pi 3B +, a designed printed circuit board and a power meter with communication via Modbus protocol. The control unit connects superior system of the smart home, electric vehicle battery management system BMS and bidirectional converter (enabling connection of the electric vehicle into the energy infrastructure of the smart home).450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Intelligent Energy Management for Microgrids with Renewable Energy, Storage Systems, and Electric Vehicles

The evolution of smart grid or smart microgrids represents a significant paradigm shift for future electrical power systems. Recent trends in microgrid systems include the integration of renewable energy sources (RES), energy storage systems (ESS), and plug-in electrical vehicles (PEV or EV). However, these integration trends bring with then new challenges for the design of intelligent control and management system. Traditional generation scheduling paradigms rely on the perfect prediction of future electricity supply and demand. They can no longer apply to a microgrid with intermittent renewable energy sources. To mitigate these problems, a massive and expensive energy storage can be deployed, which also need vast land area and sophisticated control and management. Electrical vehicles can be exploited as the alternative to the large and expensive storage. On the other hand, the use of electrical vehicles introduces new challenges due to their unpredictable presence in the microgrid. Furthermore, the utility and ancillary industries gradually adding sensors and power aware, intelligent functionality to home appliances for the efficient use of energy. Hence, the future smart microgrid stability and challenges are primarily dependent on the electricity consumption patterns of the home appliances, and EVs. Recently, demand side management (DSM) has emerged as a useful method to control or manipulate the user demand for balancing the generation and consumption. Unfortunately, most of the existing DSM systems solve the problem partially either using ESS to store RES energy or RES and ESS to charging and discharging of electrical vehicles. Hence, in this thesis, we propose a centralized energy management system which jointly optimizes the consumption scheduling of electrical vehicles and home appliances to reduce the peak-hour demand and use of energy produced from the RESs. In the proposed system, EVs store energy when generation is high or during off-peak periods, and release it when the demand is high compared to the generation. The centralized system, however, is an offline method and unable to produce a solution for a large-scale microgrid. Further, the real-time implementation of the centralized solution requires continuous change and adjustment of the energy generation as well as load forecast in each time slot. Thereby, we develop a game theoretic mechanism design to analyze and to get an optimal solution for the above problem. In this case, the game increases the social benefit of the whole community and conversely minimizes each household's total electricity price. Our system delivers power to each customer based on their real-time needs; it does not consider pre-planned generation, therefore the energy cost, uncertainty, and instability increase in the production plant. To address these issues, we propose a two-fold decentralized real-time demand side management (RDCDSM) which in the first phase (planning phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flat curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another repeated game with mixed strategy to mitigate the deviation between the immediate real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and energy storage systems and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator better deals with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. Now, it is envisioned that the presence of hundreds of microgrids (forms a microgrid network) in the energy system will gradually change the paradigms of century-old monopolized market into open, unbundled, and competitive market which accepts new supplier and admits marginal costs prices for the electricity. To adapt this new market scenario, we formulate a mathematical model to share power among microgrids in a microgrid network and minimize the overall cost of the electricity which involves nonlinear, nonconvex marginal costs for generation and T&D expenses and losses for transporting electricity from a seller microgrid to a buyer microgrid

System Design of Internet-of-Things for Residential Smart Grid

Internet-of-Things (IoTs) envisions to integrate, coordinate, communicate, and collaborate real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this paper studies the design of a large scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a backend server, unified home gateway (UHG) at the end users, and user interface for mobile devices. We discuss the features of such IoT system to support a large scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using the XMPP protocol, and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.Comment: 10 pages, 6 figures, journal pape

전기차의 하이브리드 에너지 저장장치를 위한 에너지 관리 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 장래혁.Electric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower cost per mile, higher energy efficiency and low carbon emission. However, their actual benefits are not yet clearly verified while its energy storage system (ESS) can be improved in many ways. First, low cost per mile of EV is largely diminished if we charge EV with electricity from fossil fuel power plants due to power loss during generation, transmission, conversion and charging. On the other hand, regenerative braking is direct power conversion from the wheel to battery and one of the most important processes that can enhance energy efficiency of EV. Power loss during regenerative braking can be reduced by hybrid energy storage system (HESS) such that supercapacitors accept high power as batteries have small rate capability. Second, low cost per mile claimed by EV manufacturers does not take battery depreciation into account. Battery cost takes up to 50% of the total EV price, and its life is generally guaranteed for only 8~10 years. Harsh charge and discharge profiles of a battery results in reduced cycle life. Use of HESS and systematic charge management algorithms gives potential to mitigate the problems and improve various metrics of ESS such as cycle efficiency, cycle life, and so on. This dissertation discusses design-time and run-time issues in HESS for EVs in order to maximize the energy efficiency and minimize the operating cost. This dissertation performs extensive optimization based on elaborate component models to achieve the objectives. First, we proposed systematic algorithms to maximize the energy efficiency for a regenerative braking scenario, while most of the previous works relied on empirical and heuristic methods. We improve the energy efficiency by calculating the optimal charging power distribution between the supercapacitor bank and battery bank. Minimizing the cost per mile of an EV should consider optimization over a period of time including multiple acceleration and deceleration profiles. A little forecast on the future driving profiles helps prepare the supercapacitor state of charge (SOC) to the optimal level by systematic charge migration such that it can charge and discharge the electrical energy to enhance the energy efficiency. We also propose grid power source-aware EV charging technique to minimize the electricity bill from a home equipped with photovoltaic energy generation. Lastly, we implement an actual EV equipped with HESS to verify the proposed algorithms.Chapter 1 Introduction 1 1.1 Motivation for Electric Vehicle Energy Optimization 1 1.2 ResearchContributions 4 1.3 ThesisOrganization 7 Chapter 2 Background and Related Works 8 2.1 Electric Vehicle Powertrain Generals 8 2.2 Electric Vehicle Powertrain Modeling 13 2.2.1 Vehicle Modeling 13 2.2.2 Motor and Control Circuitry . 14 2.2.3 Energy Storage Elements . 19 2.3 Hybrid Energy Storage System 23 2.3.1 Architecture 23 2.3.2 HESS Management 25 2.3.3 HESS Managementfor EV 26 2.4 Electric Vehicle Charging. 27 Chapter 3 Maximum Power Transfer Tracking for Regenerative Braking 28 3.1 Regenerative Braking of Electric Vehicles 28 3.2 Battery-Supercapacitor HESS Benefits 30 3.3 Electric Vehicle HESS SOC Management 31 3.4 Maximum Power Transfer Tracking for Regenerative Braking 32 3.4.1 Concept of Maximum Power Transfer Tracking 32 3.4.2 RegenerativeBrakingFramework 34 3.5 Experiments 36 Chapter 4 Proactive Charge Management in Electric Vehicle HESS 42 4.1 PotentialsofProactiveChargeManagement 42 4.2 Hybrid Energy Storage Systems for Electric Vehicle 43 4.2.1 EV HESS Topology 43 4.2.2 EV HESS Charge Management 44 4.3 Battery Charging and Discharging Asymmetry and Charge Migration. 47 4.4 Charge Management Efficiency Enhancement Problem 48 4.5 EV HESS Management Policy 49 4.6 Experiments 51 Chapter 5 Electric Vehicle Charging Cost Reduction 55 5.1 Electric Vehicle Charging Standards. 56 5.2 Residential Photovoltaic Installations and EV charging 58 5.3 Grid-Connected PV System with a Battery 62 5.3.1 System Architecture 62 5.3.2 Component Models 62 5.4 Electricity Bill Reduction. 65 5.4.1 Power Generation and Usage Models 65 5.4.2 Battery Management for Electricity Bill Reduction 66 5.4.3 Problem Formulation 67 5.5 Electricity Bill Optimization Algorithm 68 5.6 Experiments 73 5.7 Summary 80 Chapter 6 Electric Vehicle HESS Implementation 82 6.1 EV Prototype 82 6.1.1 Design Specifications 82 6.1.2 Motor Driver Design 85 6.1.3 Motor and Gearbox 85 6.1.4 HESS 86 6.1.5 System Monitoring subsystem 86 Chapter 7 Conclusions 88 요약 96 감사의 글 98Docto

Secure Communication Architecture for Dynamic Energy Management in Smart Grid

open access articleSmart grid takes advantage of communication technologies for efficient energy management and utilization. It entails sacrifice from consumers in terms of reducing load during peak hours by using a dynamic energy pricing model. To enable an active participation of consumers in load management, the concept of home energy gateway (HEG) has recently been proposed in the literature. However, the HEG concept is rather new, and the literature still lacks to address challenges related to data representation, seamless discovery, interoperability, security, and privacy. This paper presents the design of a communication framework that effectively copes with the interoperability and integration challenges between devices from different manufacturers. The proposed communication framework offers seamless auto-discovery and zero- con figuration-based networking between heterogeneous devices at consumer sites. It uses elliptic-curve-based security mechanism for protecting consumers' privacy and providing the best possible shield against different types of cyberattacks. Experiments in real networking environment validated that the proposed communication framework is lightweight, secure, portable with low-bandwidth requirement, and flexible to be adopted for dynamic energy management in smart grid

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