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.     

Demand side management—A simulation of household behavior under variable prices

Within the next years, consumer households will be increasingly equipped with smart metering and intelligent appliances. These technologies are the basis for households to better monitor electricity consumption and to actively control loads in private homes. Demand side management (DSM) can be adopted to private households. We present a simulation model that generates household load profiles under flat tariffs and simulates changes in these profiles when households are equipped with smart appliances and face time-based electricity prices.     

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.     

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.     

Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids

This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Network security protection technology for a cloud energy storage network controller

As part of the ongoing information revolution, smart power grid technology has become a key focus area for research into power systems. Intelligent electrical appliances are now an important component of power systems, providing a smart power grid with increased control, stability, and safety. Based on the secure communication requirements of cloud energy storage systems, this paper presents the design and development of a node controller for a cloud energy storage network. The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system. Safety protection measures were proposed according to the demands of the communication network, allowing the system to run safely and stably. Finally, the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou, China. The system was observed to operate safely and stably, demonstrating good peak-clipping and valley filling effects, and improving the system load characteristics.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

A model for generating household electricity load profiles

Electricity consumption data profiles that include details on the consumption can be generated with a bottom‐up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom‐up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42% peak reduction and sudden 3 h loss of load can be compensated with 61% mean load reduction. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

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.     

Real-time energy management in an off-grid smart home: Flexible demand side control with electric vehicle and green hydrogen production

A real-time energy management system for an off-grid smart home is presented in this paper. The primary energy sources for the system are wind turbine and photovoltaics, with a fuel cell serving as a supporting energy source. Surplus power is used to generate hydrogen through an electrolyzer. Data on renewable energy and load demand is gathered from a real smart home located in the Yildiz Technical University Smart Home Laboratory. The aim of the study is to reduce hydrogen consumption and effectively utilize surplus renewable energy by managing controllable loads with fuzzy logic controller, all while maintaining the user's comfort level. Load shifting and tuning are used to increase the demand supplied by renewable energy sources by 10.8% and 13.65% from wind turbines and photovoltaics, respectively. As a result, annual hydrogen consumption is reduced by 7.03%, and the average annual efficiency of the fuel cell increases by 4.6%     

Load management for refrigeration systems: Potentials and barriers

As a strategy to deal with the increasing intermittent input of renewable energy sources in Germany, the adaptation of power consumption is complementary to power-plant regulation, grid expansion and physical energy storage. One demand sector that promises strong returns for load management efforts is cooling and refrigeration. In these processes, thermal inertia provides a temporal buffer for shifting and adjusting the power consumption of cooling systems. We have conducted an empirical investigation to obtain a detailed and time-resolved bottom-up analysis of load management for refrigeration systems in the city of Mannheim, Germany. We have extrapolated our results to general conditions in Germany. Several barriers inhibit the rapid adoption of load management strategies for cooling systems, including informational barriers, strict compliance with legal cooling requirements, liability issues, lack of technical experience, an inadequate rate of return and organizational barriers. Small commercial applications of refrigeration in the food-retailing and cold storage in hotels and restaurants are particularly promising starting points for intelligent load management. When our results are applied to Germany, suitable sectors for load management have theoretical and achievable potential values of 4.2 and 2.8 GW, respectively, amounting to about 4–6% of the maximum power demand in Germany.     

Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture

Based on the energy storage cloud platform architecture, this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction, smooth the peak/valley difference of the load side power, and improve energy efficiency. A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers. Based on the load perception of the power grid, this study aims to investigate the operating state and service life of distributed energy storage devices. By selecting an integrated optimal control scheme, this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side. The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.     

基于云雾边协同理念的“电网一张图”维护与自动更新策略研究

为了实现智能电网“源-网-荷-储”协调互动优化运行的目标,需要对发、输、变、配、用“物理一张网”进行数字建模,构建为各个业务部门提供数据服务的“电网拓扑结构关联一张图”。文章在介绍智能电网构成和云雾边技术架构的基础上,提出了基于变电站自动化系统和智能台区分别建设能量管理系统的雾节点和边节点,对“电网一张图”提供在线维护和自动更新的策略,然后讨论了这一策略涉及到的图数据库与电力图计算、电网图模型在线更新、低压配电网拓扑结构辨识等关键技术。此策略将“源-网-荷-储”各种环节以“电网一张图”的形式紧密连接,为智能电网的高效运行提供了技术基础。     

考虑用户需求响应的电网过载风险评估与控制

智能电网的发展为用户参与电力市场运行提供了坚实的基础,根据用户需求特性与电价之间的杠杆作用,建立了用户需求弹性矩阵,结合用户需求、市场电价与系统负荷之间的关系,提出考虑用户需求响应的过载风险评估和控制方法,有效地降低电网过载的风险,并采用IEEE39节点系统仿真计算和分析验证了该方法的有效性。     

智能楼宇用电负荷优化调度

现阶段中国建筑能源消耗较大,浪费现象频现,同时,光伏等新能源分布式发电在用户侧大力推广,如何在优先消纳分布式发电的情况下管理大型楼宇用电,节约电量,降低电费,成为了近年智能楼宇建设的关键问题.对智能楼宇用电管理系统的分层架构体系进行了简单介绍,并通过对用户负荷分类,以用电经济性、舒适性和电网稳定性为目标提出了负荷优化调...     

Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.