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Home Appliances Coordination Scheme for Energy Management (HACS4EM) using Wireless Sensor Networks in Smart Grids
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In this paper, a home energy management (HEM) scheme based on appliances coordination has been proposed for future smart grids. This scheme is based on communication among home appliances, a central energy management unit (EMU), smart meter and the storage unit inside home. A wireless sensor home area network (WSHAN) using ZigBee protocol is employed for relaying messages among different entities involved in our proposed HEM scheme. The performance of WSHAN is analyzed with respect to different networking properties. HEM implementation will lead to socially and economically beneficial environment by addressing the consumers’ and utilities concerns. Increased savings, better peak load management and reduction in peak to average ratio are some of the benefits achieved by proposed scheme. Appropriate use of HEMs in a system integrated with distributed resources along with appliances co-ordination and dynamic pricing scheme provides the optimized solutions for energy management issues in smart grids as confirmed by simulation results.
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... Sector-wise installation of renewable energy sources in India by 2021 is shown in Table 1. The annual energy outlook with projections to 2035 states that an increase in the world population will lead to a 24% increase in energy demand by 2035 [4]. Residential energy consumption is the third highest among all the economic sectors that consume energy, including the transportation, industrial, and commercial sectors [5]. ...
Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.
... Finally, it has a meager delay rate, communication speed is breakneck, and it can also meet the fast transition between work and sleep [9]. ZigBee technology guarantees data storage capacity based on various network structures and optimizes and complements wireless sensor networks' energy supply and storage defects [10]. Therefore, if the ZigBee energyoptimized sensor network is added to the embedded cloud computing platform, it can improve the data monitoring system's efficiency in transmitting information and increase the storage capacity and service life of the system. ...
With the construction and development of modern and smart cities, people’s lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.
... . A summary of several papers in terms of category types.(Du and Lu 2011),,(Mahmood et al. 2014),(Ahmed et al. et al. 2015),(Sharifi and Maghouli 2019),(Khemakhem, Rekik, and Krichen 2019),(Mehrjerdi and Hemmati 2020(Pedrasa, Spooner, andMacGill 2010),(Hemmati and Saboori 2017),(Sharma, Haque, and Aziz 2019b),(S and V 2016), ...
The concept of smart homes is considered either to enhance life quality of people or to ensure energy management of buildings, where intelligent technologies are used to achieve the comfort and energy management aims in smart homes. This technology is still under fast development, and it is noticeable that a detailed research study is needed to point out state-of-the-art and future perspectives regarding smart home applications. Thus, considering the developments in smart homes, this paper investigates smart home applications in literature and market, and conducts a systematic overview by considering energy management systems and numerical researches. A comprehensive survey of smart homes is carried out to evaluate their system configurations, functional capabilities, objectives, and hardware applications in this context. Management devices, field devices, tracking systems, small appliances, and communication devices are listed as the five major hardware in the current study. Furthermore, fundamental functions of smart homes are introduced as monitoring, data logging, control, alarm/caution, and management in the current study. Subsequently, state-of-the-art of smart home technology is given to investigate the numeric values of scientific research studies, the percentage values of studies in different discipline areas, the number of scientific studies according to the nations, and the numeric values of smart home appliances. According to the numerical results, it is clear that studies on smart homes/HEMS have increased exponentially after 2000 years. The percentage values of studies in different discipline areas and the number of studies conducted by the leading countries interested in smart homes/HEMS are conducted in this work. The current study also analyzes how the future perspective of smart home technology has been shaped over the years. According to the future perspectives, the numeric values show that the number of smart home applications and their market value are expected to grow in the near future, where smart appliances and market budget are expected to be 75.4 billion units 262.8 billion dollars by 2025, respectively.
... Their scheduling process is separated into three phases for appliance monitoring, stochastic scheduling, and the real-time control of appliances. An approach based on devices that communicate using the ZigBee protocol is presented in [36]. The scheme reduces the overall costs for energy consumption by shifting appliances to off-peak hours. ...
The transformation of energy grids towards smart grids is driven by numerous political, economic, and ecological goals. As part of this process, the centralized top-down architecture of energy grids changes towards increasingly decentralized structures. It is widely accepted that the challenges emerging from this transition threaten the resilient operation of energy grids. For instance, the volatility of renewable energy sources challenges the required balance between demand and supply; their distribution in the energy grid likewise complicates their coordination. Holarchies are a promising (systems-of-systems) architectural pattern for smart grids fostering fast isolation and self-sustained operation of subparts (so-called holons), as well as supporting dynamic reconfigurations of the grid’s structure. To leverage these properties to increase the resilience of smart grids, we propose a system model that combines a holonic architecture and locally available resources offered by prosumers. Our model organizes the participants in the grid as holarchy and enables the application of fine-grained control mechanisms. We show the capabilities of the model by resolving an overproduction situation and a situation of severe electricity scarcity using a modified binary ant colony optimization approach. Our evaluation with the simulation environment HOLEG shows that the system model and the proposed algorithm can quickly mitigate balancing problems in holonic energy grids.
... Smart appliances change household electricity demand. A wireless sensor domestic area network working on ZigBee protocol, utilized for transmitting messages between different units in a household energy management system established on appliances coordination, can offer improved resolutions for energy management problems [16]. ...
A smart grid is an intellectual electricity grid that enhances the production, distribution and consumption of electricity across the insertion of Information and Communication Technologies on the electricity grid. In quintessence, smart grids bring profound modifications in the information systems that drive them, new data streams coming from the electricity grid, new participants such as regionalized producers of renewable energies, new uses such as electric vehicles and connected homes and new communicating equipment such as smart meters, sensors and remote control points. Altogether, this will result in an overflow of information, which the energy corporations will have to handle. Big data technologies propose proper solutions for utilities, although the decision regarding which big data technology to deploy is critical. In this paper, we present an overview of data management for smart grids, summarize the enhanced value of big data technologies for this type of data, and discuss the technical obligations, tools and key steps to employ big data solutions in the smart grid framework.
The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.
The review this article conducts is an extensive analysis of the concept of a smart grid framework with the most sophisticated smart grid innovation and some basic information about smart grid soundness. Smart grids as a new scheme for energy and a future generation framework encourages the expansion of information and progress. The smart grid framework concord will potentially take years. In this article, the focus is on developing smart networks within the framework of electric power systems.
This paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed framework.
The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.
Pakistan is facing severe energy crisis in spite of the fact that nature has blessed her with huge energy potential. Short fall of electricity supply in the country is increasing and has been recorded up to 4522 MW in 2010. This deficit reached to 7000 MW in May, 2011. A comprehensive review of Pakistan's energy sector is presented in this paper. Energy potential, major issues of energy sector and energy import options are discussed. Issues like poor management, combined cycle capacity, low hydro power share, circular debt and energy security have been covered. Energy potential assessment includes hydro solar, wind, coal, nuclear, hydrogen cells, geo-thermal, ocean resources and bio mass. Future prediction calculations are based upon country's current and world's average per capita energy consumption. Current oil and gas reserves of the country contribute to only 5 percent and 48.8 percent of the energy mix and at the current rate will be exhausted by 13 and 16 years respectively. The overwhelming dependence of the energy sector on imported fossil fuels may create a situation of energy security threat. However dependence upon the energy import options cannot be avoided in order to lessen the severity of energy crisis in near future. Some of the energy import options are: Turkmanistan, Afghanistan, Pakistan and India (TAPI); Iran, Pakistan and India (IPI) gas pipelines; Liquefied Natural Gas (LNG) from Qatar etc. On the other hand exploitation of vast renewable potential like hydro, solar and wind requires serious attention. Exploitation of indigenous coal resources would also be a key for solving energy crisis in the long run. In summary, this paper presents energy potential assessment in context of major issues, future predictions and impact of energy import options. This in turn provides a big, clear and brighter picture of the country's energy sector.
Energy management system for efficient load management is presented in this paper. Proposed method consists of the two main parts. One is the energy management center (EMC) consisting of graphical user interface. EMC shows the runtime data and also maintains the data log for the user along with control of the appliances. Second part of the method is load scheduling which is performed using the single knapsack problem. Results of the EMC are shown using LABVIEW while MATLAB simulations are used to show the results of load scheduling. Hardware model is implemented using human machine interface (HMI). HMI consists of PIC18f4520 of microchip family and zigbee transceiver of MC12311 by Freescale. The microcontroller interface with the zigbee transceiver is on standard RS232 interface. INDEX TERMS—Smart Grid, Energy Management, Zigbee.
With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.
This paper describes more efficient home energy management system to reduce power consumption in home area. We consider the room easily controllable with an IR remote control of a home device. The room has automatic standby power cut-off outlets, a light, and a ZigBee hub. The ZigBee hub has an IR code learning function and educates the IR remote control signal of a home device connected to the power outlet. Then the power outlets and the light in the room can be controlled with an IR remote control. A typical automatic standby power cut-off outlet has a waiting time before cutting off the electric power. It consumes standby power during that time. To eliminate the waiting time, we turn off the home device and the power outlet simultaneously with an IR remote control through the ZigBee hub. This method actively reduces the standby power. The proposed HEMS provides easy way to add, delete, and move home devices to other power outlets. When a home device is moved to the different outlet, the energy information of the home device is kept consistently and seamlessly regardless of location change. The proposed architecture gives more efficient energy-saving HEMS.
This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes or buildings, aiming at reducing the peak load in them as well as in the system-wide power transmission network. Following the task model consist of actuation time, operation length, deadline, and a consumption profile, the scheduler linearly copies the profile entry or maps a combinatory vector to the allocation table one by one according to the task type, which can be either preemptive or nonpreemptive. The proposed scheme expands the search space recursively to traverse all the feasible allocations for a task set. A pilot implementation of this scheduling method reduces the peak load by up to 23.1 % for the given task set. The execution time, basically approximated by O(M NNP(3 M/2) NP) where M, N NP, and N P are the number of time slots, nonpreemptive tasks, and preemptive tasks, respectively, is reduced almost to 2% taking advantage of an efficient constraint processing mechanism which prunes a search branch when the partial peak value already exceeds the current best. In addition, local peak reduction brings global peak reduction by up to 16% for the home-scale scheduling units without any global coordination, avoiding uncontrollable peak resonance.
In this paper, we propose a novel optimization-based real-time residential load management algorithm that takes into account load uncertainty in order to minimize the energy payment for each user. Unlike most existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowing some statistical estimates of the future load demand. Moreover, we consider real-time pricing combined with inclining block rate tariffs. In our problem formulation, we take into account different types of constraints on the operation of different appliances such as must-run appliances, controllable appliances that are interruptible, and controllable appliances that are not interruptible. Our design is multi-stage. As the demand information of the appliances is gradually revealed over time, the operation schedule of controllable appliances is updated accordingly. Simulation results confirm that the proposed energy consumption scheduling algorithm can benefit both users, by reducing their energy expenses, and utility companies, by improving the peak-to-average ratio of the aggregate load demand.
We present a detailed review of various Home Energy Management Schemes
(HEM,s). HEM,s will increase savings, reduce peak demand and Pto Average Ratio
(PAR). Among various applications of smart grid technologies, home energy
management is probably the most important one to be addressed. Various steps
have been taken by utilities for efficient energy consumption.New pricing
schemes like Time of Use (ToU), Real Time Pricing (RTP), Critical Peak Pricing
(CPP), Inclining Block Rates (IBR) etc have been been devised for future smart
grids.Home appliances and/or distributed energy resources coordination (Local
Generation) along with different pricing schemes leads towards efficient energy
consumption. This paper addresses various communication and optimization based
residential energy management schemes and different communication and
networking technologies involved in these schemes.
Global energy crises are increasing every moment. Every one has the attention
towards more and more energy production and also trying to save it. Electricity
can be produced through many ways which is then synchronized on a main grid for
usage. The main issue for which we have written this survey paper is losses in
electrical system. Weather these losses are technical or non-technical.
Technical losses can be calculated easily, as we discussed in section of
mathematical modeling that how to calculate technical losses. Where as
nontechnical losses can be evaluated if technical losses are known. Theft in
electricity produce non-technical losses. To reduce or control theft one can
save his economic resources. Smart meter can be the best option to minimize
electricity theft, because of its high security, best efficiency, and excellent
resistance towards many of theft ideas in electromechanical meters. So in this
paper we have mostly concentrated on theft issues.
The paper intends to give a contribution toward the definition of a fully decentralized voltage quality monitoring architecture by proposing the employment of self organizing sensor networks. According to this paradigm each node can assess both the performances of the monitored site, computed by acquiring local information, and the global performances of the monitored grid section, computed by local exchanges of information with its neighbors nodes. Thanks to this feature each node could automatically detect local voltage quality anomalies. Moreover system operator can assess the system voltage quality index for each grid section by inquiring any node of the corresponding sensors network without the need of a central fusion center acquiring and processing all the node acquisitions. This makes the overall monitoring architecture highly scalable, self-organizing and distributed.
Wireless Sensor Networks (WSN) are getting more integrated to our daily lives and smart surroundings as they are being used for health, comfort and safety applications. In smart homes and office environments, WSNs are generally used to increase the inhabitant comfort. As the current energy grid is evolving into a smart grid, where consumers can directly reach and control their consumption, WSNs can take part in domestic energy management systems, as well. In this paper, we propose the Appliance Coordination (ACORD) scheme, that uses the in-home WSN and reduces the cost of energy consumption. The cost of energy increases at peak hours, hence reducing the peak demand is a major concern for utility companies. The ACORD scheme, aims to shift consumer demands to off-peak hours. Appliances use the readily available in-home WSN to deliver consumer requests to the Energy Management Unit (EMU). EMU schedules consumer requests with the goal of reducing the energy bill. We show that ACORD decreases the cost of electricity usage of home appliances significantly.