Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

A generic demand‐side management model for smart grid

The demand‐side management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost and per unit cost. The main objective of this research article is to develop a generic demand‐side management (G‐DSM) model for residential users to reduce peak‐to‐average ratio (PAR), total energy cost, and waiting time of appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G‐DSM model is based on genetic algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G‐DSM model for both single and multiple user scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.     

A new IoT‐based smart energy meter for smart grids

The significant increase in energy consumption by the growth of the population or by the use of new equipment has brought big challenges to the energy security as well as the environment. There is a need that consumers can track their daily use and understand consumption standards for better organizing themselves to obtain financial and energetic efficiency. With the improvement of smart networks technology for better energy supply, a smart meter is not just a simple measurement gadget anymore, but it has additional functions including smart equipment control, bidirectional communication that allows integration of users and networks, and other functionalities. Smart meters are the most fundamental components in smart power grids. Besides, the meters used with a management system can be utilized for monitoring and controlling home appliances and other gadgets according to the users' need. A solution of an integrated and single system should be more efficient and economical. Smart measurement systems allow monitoring the energy consumption of the final consumers while providing useful information about the energy quality. The information provided by these systems is used by the operators to enhance the energy supply, and different techniques can be also applied for this end, such as charge scheduling, management from the demand side, and non‐intrusive load monitoring. The Internet of Things (IoT) is becoming a great ally in the management of smart distribution and energy consumption in smart systems scenarios. To address these issues, this paper proposes and demonstrates a new smart energy meter following an IoT approach and its associated costs and benefits. The developed device incorporates several communication interfaces. In order to easily integrate with any monitoring software solution, the meter has a multi‐protocol connection. Finally, the provided solution is validated and demonstrated in real‐life environments and it is also under use.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

A review on the features and technologies for energy efficiency of smart grid

In today's world striving for efficiency in every sector, especially power generation and distribution, smart grids emerge as the solution for efficiently meeting the increasing demand. They adjust themselves to optimally deliver energy at the lowest cost and highest quality possible. The grid successfully makes use of renewable energy resources, electric vehicles, and smart pricing techniques in its attempt to achieve energy efficiency. It also promotes a greener environment by striving to reduce greenhouse gas emissions. Information communication technology (ICT) helps the grid in collecting consumption data from the consumers and in sharing tariff information. ICT also helps to gather information about the status of the grid with regard to aspects like power quality, faults etc. The purpose of this paper is to review recent literature with a view to comprehensively present the technologies employed in the smart grid for achieving energy efficiency and the challenges involved therein.     

Alternative ways for voltage control in smart grids with distributed electricity generation

In this paper, we have studied different strategies for managing voltage fluctuations in distribution networks originating from decentralized electricity generation systems (DEGS) or increased loads, which are highly important issues in the smart grid context. A starting point for system design when increasing load or local power production could be to limit the voltage fluctuations to ±5% from nominal voltage. Strategies to regulate voltage include cable improvement, transformer management, Demand side management, storage, and line interconnection. We present a mathematical model applicable for both a static and dynamic analysis to quantify effects from these measures, though the best solution will depend on local conditions and needs to be determined case by case. Combining several voltage control options simultaneously may lead to further positive effects. Strategies when doubling the load and increasing DEGS production to twice the electricity demand were analyzed here in detail. It also needs to be pointed out that other factors related to power quality besides voltage may need consideration when large amounts of DEGS are integrated to distribution networks. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal operation of DC smart house system by controllable loads based on smart grid topology

From the perspective of global warming mitigation and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all-electric apartment houses or residence such as DC smart houses are increasing. However, due to the fluctuating power from renewable energy sources and loads, supply-demand balancing of power system becomes problematic. Smart grid is a solution to this problem. This paper presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuation. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuation, it is possible to reduce the electric power consumption and the cost of electricity. This system consists of photovoltaic generator, heat pump, battery, solar collector, and load. To verify the effectiveness of the proposed system, results are used in simulation presented.     

A hybrid optimization technique for proficient energy management in smart grid environment

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively.     

Energy management system for smart grid: An overview and key issues

Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug-in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug-in electric vehicles, the electricity price, and the time-varying load bring new challenges to the power engineers to achieve demand-supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand-side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting-edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.     

以智能化电网的要求规划上海市负荷管理系统再发展

介绍了上海市用电负荷管理系统的现状,研究了230 MHz通信瓶颈问题。在对当前建设智能化电网负荷管理系统面临的若干问题进行分析与研究的基础上,依据智能化电网发展的要求,提出了有效利用现有资源,提高通信和数据安全水平,统一数据融合和技术标准,增强与客户互动机制和体系,进一步推进负荷管理系统可持续发展的方案及建议。     

Modelling electricity storage systems management under the influence of demand‐side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market‐clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off‐peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand‐side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off‐peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off‐peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable. Copyright © 2008 John Wiley & Sons, Ltd.     

智能电网中PEMFC分布式电源的建模及性能分析

采用风能、太阳能、燃料电池等清洁能源发电的分布式电源是智能电网的重要组成部分。质子交换膜燃料电池PEMFC作为分布式发电的一种,具有能量密度高,环境污染小,噪音低,启动快,工作温度低等优点。文中建立了PEMFC燃料电池的数学模型,采用Simulink软件搭建了仿真模型,并对PEMFC燃料电池在不同温度及气体压力下的发电效率进行了分析。     

An energy storage system for smart coal mine emergency power supply

为满足煤炭行业和煤矿企业对于供电可靠性日益增长的需求,同时探索兆瓦级储能系统在工业用户侧的实用化解决方案,本项目在内蒙古乌海平沟煤矿设计建造了基于铅酸电池和磷酸铁锂电池储能技术的矿用兆瓦级智能应急电源。系统主要功能为：在电网正常供电时,替代传统的油浸电容器进行无功补偿;在电网出现供电故障时,为煤矿的特别重要负荷提供至少30 min的连续可靠供电。除此外,系统还可根据用户需求执行包括削峰填谷、分布式新能源发电波动平抑在内的多种功能。为保证应急电源系统的安全性、可靠性和使用寿命,本工作在进行设计时着重考虑了蓄电池的选型、容量配比、成组设计以及储能变流系统（PCS）的电路拓扑设计和电池维护高级智能控制策略,旨在探索和实用。     

Analyses of hydrogen energy system as a grid management tool for the Hawaiian Isles

One of the objectives of the research project at Hawaii Natural Energy Institute (HNEI) is to demonstrate long-term durability of the electrolyzer when operated under cyclic operation for frequency regulation on an Island grid system. In this paper, a Hydrogen Energy System with an electrolyzer is analyzed as a potential grid management tool. A simulation tool developed with a validated model of the hydrogen energy system and Island of Hawaii grid model is presented and employed for this investigation. The simulation study uses realistic measured solar and wind power profiles to understand what optimal electrolyzer size would be required to achieve the maximum level of grid frequency stabilization. The simulation results give insight into critical information when designing a hydrogen energy system for grid management applications and the economic impact it has when operated as a pure grid management scheme or as a limitless hydrogen production system.     

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi‐oppositional‐chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid‐connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two‐strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.     

A multi-agent system for energy management of distributed power sources

The field of energy management is an area increasingly studied. However, most solutions are based on centralized systems and barely fulfil criterion like fault tolerance or adaptability. Also, these systems are often difficult to design because of the “top–down” approach used: the designer generally knows how each component has to respond separately, but a centralized management system focuses his attention solely on the overall reaction of the system. That is why a distributed management solution based on the paradigm of Multi-Agent Systems (MASs) is proposed in this paper. In addition to a more natural conception, based on a “bottom–up” approach, this solution ensures better system reliability. After reviewing the previous works, an application of MAS to power management in a hybrid power source is presented. Then, the system is tested using a simulation model. The results show that this approach is perfectly valid and can respond to most problems of centralized energy management systems (EMSs).     

A comparative analysis of energy and CO2 taxes on the primary energy mix for electricity generation

In many countries, economies are moving towards internalization of external costs of greenhouse‐gas (GHG) emissions. This can best be achieved by either imposing additional taxes or by using an emission‐permit‐trading scheme. The electricity sector is under scrutiny in the allocation of emission‐reduction objectives, not only because it is a large homogeneous target, but also because of the obvious emission‐reduction potential by decreasing power generation based on carbon‐intensive fuels. In this paper, we discuss the impact of a primary‐energy tax and a CO₂ tax on the dispatching strategy in power generation. In a case study for the Belgian power‐generating context, several tax levels are investigated and the impact on the optimal dispatch is simulated. The impact of the taxes on the power demand or on the investment strategies is not considered. As a conclusion, we find that a CO₂ tax is more effective than a primary‐energy tax. Both taxes accomplish an increased generation efficiency in the form of a promotion of combined‐cycle gas‐fired units over coal‐fired units. The CO₂ tax adds an incentive for fuel switching which can be achieved by altering the merit order of power plants or by switching to a fuel with a lower carbon content within a plant. For the CO₂ tax, 13 €/tonCO₂ is withheld as the optimal value which results in an emission reduction of 13% of the electricity‐related GHG emissions in the Belgian power context of 2000. A tax higher than 13 €/tonCO₂ does not contribute to the further reduction of GHGs. Copyright © 2005 John Wiley & Sons, Ltd.