Monitoring and Controlling Power using Zigbee Communications

Smart grid is a modified form of electrical grid where generation, transmission, distribution and customers are not only connected electrically but also through strong communication network with each other as well as with market, operation and service provider. For achieving good communication link among them, it is very necessary to find suitable protocol. In this paper, we discuss different hardware techniques for power monitoring, power management and remote power controlling at home and transmission side and also discuss the suitability of Zigbee for required communication link. Zigbee has major role in monitoring and direct load controlling for efficient power utilization. It covers enough area needed for communication and it works on low data rate of 20Kbps to 250Kbps with minimum power consumption. This paper describes the user friendly control home appliances, power on/off through the internet, PDA using Graphical User Interface (GUI) and through GSM cellular mobile phone.Comment: 5th International Workshop on NGWMN with 7th IEEE International Conference on BWCCA 2012, Victoria, Canada, 201

Desain Real-Time Monitoring Berbasis Wireless Sensor Network Upaya Mitigasi Bencana Erupsi Gunung Api

Indonesia yang terletak dengan kondisi geografis tempat pertemuan lempenglempeng Litosfir, lempeng Indo-Australia, lempeng Eurasia dan lempeng Pasifik membuat lapisan bebatuan yang ada di Indonesia dilalui deretan pegunungan muda Mediterania yang merupakan gugusan pegunungan Himalaya, menjadikan Indonesia menjadi salah satu negara yang memiliki jumlah gunungapi aktif terbanyak di dunia. Dari 127 gunungapi yang aktif yang ada di Indonesia, 70 gunungapi diantaranya yang bisa dipantau oleh Pusat Vulkanologi dan Mitigasi Bencana Geologis. Dengan kehadiran teknologi Wireless Sensor Network (WSN), yaitu sebuah teknologi jaringan sensor tanpa kabel dengan transmisi data menggunakan standar protokol IEEE 802.15.4/Zigbee, dengan device Xbee Pro Series, bisa menjadi salah satu alternatif untuk menjadikan sebagai alat bantu dalam memonitoring gunung-gunung aktif yang ada. Tujuan pembuatan perangkat komunikasi ini adalah sebagai perangkat yang digunakan untuk melakukan transmisi data hasil dari pengindraan yang dilakukan oleh sensor temperature dan gas. Dalam perangkat ini juga terintegrasi didalamnya sebuah mikrokontroler Arduino Uno yang berbasis ATMega 328 yang berfungsi untuk menggelolah data. Dari hasil pengujian sistem didapatkan bahwa pada saat kondisi Line of Sight (LOS) jangkau transmisi terjauh adalah sebesar 500 meter. Sedangkan untuk kondisi Non Line of Sight (NLOS) jarak jangkauan maksimal untuk paket data bisa terkirim adalah sejauh 25 meter dengan penghalang berupa bahan tembok beton dengan ketebalan beton 15 cm. Dari hasil pengujian througput yang didapatkan untuk jarak 100 sampai 200 meter banyaknya data yang diterima oleh server node bisa mencapai nilai 2,1 KBps, bila jaraknya ditambah menjadi 300 meter sampai 500 meter nilai througput nya sebesar 1,80 sampai dengan 1,23 KBps, sedangkan untuk jarak 500 meter hanya mencapai nilai 0,836 KBps. ================================================================================================================== The Geographical Location of Indonesia is place that meeting plates, They are Lithosphere Plate, Indo-Australian Plate, The Eurasian Plate and the Pacific Plate to make a ring of fire that exist in Indonesia pass through a row of The Young Mediterranean Mountains, Which owns The Cluster of the Himalayas makes Indonesia, The Most Dangerous Country who have lot of number of active volcano in The World. From The 127’s active volcano in Indonesia, Just only 70 volcanoes which can be monitored by the Center for Volcanology and Geological Hazard Mitigation (PVMBG). With The Technologycal improvemnet of Wireless Sensor Network (WSN), Which is a wireless sensor network technology with data transmission using a standard protocol IEEE 802.15.4 / Zigbee, with XBee Pro Series device, can be an alternative to make as monitoring tools the mountain- active volcano over there. The purpose of making these communication devices is a device used to transmit data from the results of sensing performed by the temperature sensor and gas. In this device also integrates therein an Arduino Uno microcontroller based ATMega 328 that serves to menggelolah data. From the test results showed that the system at the time the condition of Line of Sight (LOS) transmission range is 500 meters farthest. As for the condition of Non Line of Sight (NLOS) range of the maximum distance to data packets can be sent is 25 meters with a barrier material such as concrete walls with a thickness of 15 cm concrete. Throughput of the test results obtained for a distance of 100 to 200 meters show the amount of data received by the server node can reach a value of 2.1 Bps. When the distance is increased or changed from 300 to 500 meters that throughput values have range of 1.80 to 1.23 KBps, Where as for a distance of 500 meters that test result showed could be reach only 0,836 KBps

Optimal Operation of Power Distribution Feeders with Smart Loads

Distribution systems have been going through significant changes in recent years, moving away from traditional systems with low-level control toward smart grids with high-level control, with improved technologies in communications, monitoring, computation, and real-time control. In the context of smart grids, Demand Response (DR) programs have been introduced so that customers are able to control and alter their energy consumption in consideration with distribution system operators, with benefits accruing to both customers and Local Distribution Companies (LDCs). This thesis focuses on the integration of DR with the intelligent operation of distribution system feeders. Thus, it proposes a mathematical model of an unbalanced three-phase distribution system power flow, including different kinds of loads and other components of distribution systems. In this context, an unbalanced three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which includes the models of lines, transformers, voltage-based loads, smart loads, Load Tap Changers (LTCs), and Switched Capacitors (SCs), together with their respective operating limits, to determine the optimal switching decisions for LTCs, SCs, and control signals for smart loads, in particular, Energy Hub Management System loads and Peaksaver PLUS loads. Hence, Neural-Network-based models of controllable smart loads, which are integrated into the DOPF model are proposed, developed, and tested. Since the DOPF model has different discrete variables such as LTCs and SCs, the model is a Mixed-Integer Non-Linear Programming (MINLP) problem, which presents a considerable computational challenge. In order to solve this MINLP problem without approximations and ad-hoc heuristics, a Genetic Algorithm (GA) is used to determine the optimal control decisions of controllable feeder elements and loads. Since the number of control variables in a realistic distribution system is large, solving the DOPF for real-time applications using GA is computationally expensive. Hence, a decentralized system with parallel computing nodes based on a Smart Grid Communication Middleware (SGCM) system is proposed. Using a "MapReduce" model, the SGCM system executes the DOPF model, communicates between the master and the worker computing nodes, and sends/receives data amongst different parts of the parallel computing system. When large number of nodes are involved, the SGCM system has a fast performance, is reliable, and is able to handle different fault tolerance levels with the available computing resources. The proposed approaches are tested and validated on a practical feeder with the objective of minimizing energy losses and/or energy drawn from the substation. The results demonstrate the feasibility of the developed techniques for real-time distribution feeder control, highlighting the advantages of integration of smart loads in the operation of distribution systems by LDCs

Optimal Operation of Energy Hubs in the Context of Smart Grids

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints

Optimal Design and Planning of Energy Microgrids

Microgrids are local energy providers which reduce energy expense and gas emissions by utilising distributed energy resources (DERs) and are considered to be promising alternatives to existing centralised systems. However, currently, problems exist concerning their design and utilisation. This thesis investigates the optimal design and planning of microgrids using mathematical programming methods. First, a fair economic settlement scheme is considered for the participants of a microgrid. A mathematical programming formulation is proposed involving the fair electricity transfer price and unit capacity selection based on the Game-theory Nash bargaining approach. The problem is first formulated as a mixed integer non-linear programming (MINLP) model, and is then reformulated as a mixed integer linear programming (MILP) model. Second, an MILP model is formulated for the optimal scheduling of energy consumption of smart homes. DER operation and electricity consumption tasks are scheduled based on real-time electricity pricing, electricity task time windows and forecasted renewable energy output. A peak charge scheme is also adopted to reduce the peak demand from the grid. Next, an MILP model is proposed to optimise the respective costs among multiple customers in a smart building. It is based on the minimisation/maximisation optimisation approach for the lexicographic minimax/maximin method, which guarantees a Pareto-optimal solution. Consequently each customer will pay a fair energy cost based on their respective energy consumption. Finally, optimum electric vehicle (EV) battery operation scheduling and its related degradation are addressed within smart homes. EV batteries can be used as electricity storage for domestic appliances and provide vehicle to grid (V2G) services. However, they increase the battery degradation and decrease the battery performance. Therefore the objective is to minimise the total electricity cost and degradation cost while maintaining the demand under the agreed threshold by scheduling the operation of EV batteries

Home Control Via Mobile Devices: State Of The Art And Hci Challenges Under The Perspective Of Diversity

With technological advancements in recent decades, home environments incorporate several electronic appliances in order to facilitate activities and improve users' quality of life. The increasing complexity of household appliances makes their management a nontrivial task. Mobile devices emerge as excellent platforms to enable control over such a range of appliances, providing convenience, flexibility and several interaction possibilities. However, home control via mobile devices also present some challenges, among others regarding the diversity of users. This paper presents the state of the art in home control via mobile devices. Based on an analysis of these solutions, we identify and discuss Human-Computer Interaction (HCI) challenges under the perspective of diversity. In addition, we propose a set of guidelines in order to minimize or overcome these challenges. 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