Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Smart Grids with Intelligent Periphery:An Architecture for the Energy Internet

A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energysharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system （EMS）-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of （1） dispatch, （2） smoothing, and （3） mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.

Renewable and Storage Energy Integration for Smart Grid Housing

This paper presents the design, implementation and testing of an embedded system that integrates solar and storage energy resources to smart homes within the smart mierogrid. The proposed system provides the required home energy by installing renewable energy and storage devices. It also manages and schedules the power flow during peak and off-peak periods. In addition, a two-way communication protocol is developed to enable the home owners and the utility service provider to improve the energy flow and the consumption efficiency. The system can be an integral part for homes in a smart grid or smart microgrid power networks. A prototype for the proposed system was designed, implemented and tested by using a controlled load bank to simulate a scaled random real house consumption behavior. Three different scenarios were tested and the results and findings are reported. Moreover, data flow security among the home, home owners and utility server is developed to minimize cyber-attaeks.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

智慧城市电网电子化移交自动管控系统

当前对不同类型电力数据无同步处理,电力数据移交任务中成功转换的次数减少,移交时间长,为此设计智慧城市电网电子化移交的自动管控系统。以服务形式向多个业务中心转移数据,控制电网电子驱动电流和电压,完成恒定功率下的快速响应,通过应答通讯电网数据移交模块回应不同电量下电网数据指令,平衡处理智慧电网电子化移交数据,构建电网神经网络信息移交模型。测试结果表明,当电力数据移交执行次数设定为500次时,所设计系统能够一次性执行成功,任务移交时间为5.2 s,而对比系统需要进行五次任务转换,所需时间分别为16.4 s和21.2 s,该系统的优势比较明显。

基于多模态生物特征识别的高校门禁系统设计与实现

为解决传统门禁系统对实体校园卡的过度依赖、数据应用能力差、无法形成精细化管理等问题,在已有门禁系统架构基础上,基于一卡通专网,采用分布式架构设计了一种基于多模态生物特征识别的高校智慧门禁系统。该系统集采集管理、生物数据库、算法服务、校门口出入管理、访客出入管理、公寓管理、设备管理和出入权限管理等功能于一体,实现了校园出入口的智慧化管理,提升了校园安全管理效率,为下一步的智慧校园建设奠定了应用基础。

A wireless intelligent thermal control and management system for piglet in large-scale pig farms

Indoor temperature is a critical environmental factor for piglets that affects the health,welfare,and production efficiency of domesticated pigs.However,there are limited reports about wireless intelligent thermal control and management system for piglets in largescale pig farms,and evaluation energy saving.This paper presents an Automatic Thermal Control and Management System(ATCMS)of micro-environment for piglets,that could provide a suitable production environment and reduce the energy consumption.ATCMS includes automatic temperature control system(ATCS),multipoint temperature management system(MTMS)and remote access.The infrared heating lamps are chosen in the ATCS for their good performance in heating rate and heating distribution.In order to meet a scientific temperature requirements of piglet growth,the suitable thermal ranges of each growth-stage(STREG)are employed as intelligent control strategies to generate an automatic and precise heating control.MTMS is responsible for communicating with ATCSs and transmitting information to database server for record based on wireless technology.Remote access offers information services for remote administrators by smart phones and clients via Internet.The experiments of ATCMS were carried out both in winter and summer at Binsheng Breeding Piggery Farm,Acheng City,Heilongjiang Province,China.Results showed that the temperature provided by ATCS had been kept in STREG of piglets during the whole winter period.When the indoor temperature during summer period were in or above STREG,heating lamps were turned off by ATCS for a long time.The energy consumption of ATCS were 63.60%in winter and 39.35%in summer with continuous heating system operation.Therefore,ATCMS could take the suitable thermal ranges of each growth-stage as an intelligent control principle to give an automatic and precise control to heating devices in order to meet a scientific temperature requirements of piglet growth.

医院高效机房改造:溴化锂机组向磁悬浮机组的转型

以宁波大学附属妇女儿童医院高效机房改造为例,将溴化锂机组替换为磁悬浮冷水机组,达到节能降耗效果。通过对医院原有溴化锂机组系统的深入分析,揭示其存在能效低下和蒸汽成本高等问题。并详细阐述了磁悬浮机组的优势及改造方案,包括主机选型、系统配置调整及智慧能源管控平台搭建。对改造后的综合性能进行评估,结果表明经济、性能及节能成效显著,可供医院节能改造参考,具有推广价值。

丘陵山地智能混动割草机控制系统设计

针对我国高原和丘陵山地果园、茶园等农业生产活动中,生草制管理所面临的割草机械作业效率低、时间短及自动化程度低的问题,结合自主设计的丘陵山地智能混动割草机,设计一种可实时识别工况并自动完成能量管理的割草机控制系统。该系统以割草机实时接收的行驶工况识别信号为切入点,采用实时工况识别算法程序,依据不同类别工况的特点采取相应的能量管理控制策略。通过搭建控制系统并结合作业工况,进行该控制系统的性能试验。试验结果表明:该控制系统能够快速、准确的响应割草机动力性与割草性能要求,且相比于同耗油量下无能量管理的油电混合割草机,其单次作业时间约增加18%。同时验证该工况识别系统的合理性,工况正确识别时长精度达到90%以上。

电石法聚氯乙烯信息化智能化实践

聚焦电石法聚氯乙烯信息化的主要问题,通过定制化开发与工业应用MES信息化管理系统、生产工艺自动控制系统、设备智能监测系统以及能源管理信息化平台,提高了生产运行和能源管理效率,取得了良好的实践效果。

绿色装配式智慧公共厕所自动控制技术研究

文章从自动控制技术角度,探讨了绿色装配式智慧公共厕所的建设理念和技术体系。通过文献综述法分析智慧公共厕所的功能需求;应用系统分析法,从环境监测与调控、资源管理、卫生清洁等方面,详细论证了智慧公厕自动控制体系的具体技术应用。研究结果显示,该自动控制体系能够显著改善公厕环境质量和服务水平,实现节能环保。

碳中和背景下的智慧能源管理路径探究

在碳中和的背景下,引入智慧能源管理不仅可以提升能源管理的效率和水平,更能推动能源结构的转型升级,促进经济的可持续发展。文中首先分析了传统的能源系统及能耗在线监测系统的不足之处,指出了政府引入智慧能源管理系统的必要性。随后,选取了一栋典型办公建筑作为研究对象,对不同智能控制策略下的能源管理进行了对比分析。研究发现,引入智慧能源管理策略,可以有效提升电力调度次数,增强能源管理效果,极大地降低电力、燃气、水资源的能源消耗。基于此,文中创新设计了一款碳中和背景下的智慧能源管理系统,并介绍了其系统架构与优势。结果表明,该系统有助于政府在碳中和背景下实现更加智慧、高效的能源管理,推动可持续发展和碳减排目标的实现。

新能源升压站智慧运维技术研究与实践

随着全球能源结构的转型,新能源风电和光伏场站的智慧运维,成为了当前新能源领域的重要研究方向。传统新能源升压站地理位置偏,日常巡检工作主要依靠人工现场进行数据分析,运维难度大。升压站运维业务水平要求高,站内各系统之间存在信息孤岛,设备故障率普遍高于电网,面对故障缺乏标准的处理建议。文章分析新能源升压站运维管理现状,研究新能源升压站智慧运维技术,以真正做到新能源升压站智能化、数字化运检,实现精益化管理,提升管理效率,降低运维成本,满足“无人值班,少人值守、大数据提升”智能化综合管控需求。

智能电网调度控制系统AGC需求分析及关键技术

对智能电网调度控制系统中自动发电控制(AGC)面临的新业务需求及关键技术进行了分析和展望。剖析了AGC控制模式、性能评价标准和控制策略的内涵,论述了其在特高压建设、一体化调度模式和新能源接入背景驱动下的变化;对智能调度AGC领域已开展的技术实践进行了总结和思考,提出了适应新变化的控制架构和关键技术;最后,探讨了AGC为满足未来华北、华中、华东(简称"三华")同步电网控制需解决的若干技术问题。

智能电网控制中心技术的未来发展

对智能电网控制中心技术进行了分析和展望。分析了电力系统中电力流、信息流、资金流的变化规律和相互之间的制约关系。通过对信息流的调控,改善电力流和资金流,实现智能电网。以信息流为基础,从信息分层、上下层信息互动、不同时间尺度信息之间的协调几个方面研究了智能电网调度控制系统构架。从空间、时间、控制目标等3维协调、发展,以及基于相量测量单元的动态管理系统、系统级的闭环控制等方面探讨智能电网控制中心应用软件可能发生的变革,并对如何适应特高压电网的智能电网控制中心技术进行了讨论。

吉林电网风电调度自动化系统设计

提出了吉林电网风电调度自动化系统的设计方法,该方法要求风电场上报发电计划,省调据此将风电纳入到日前开机计划和日内电力电量平衡中,同时考虑电网的各种约束条件,编制风电场的发电计划曲线并制订风电大规模接入地区的自动电压控制(AVC)策略,风电场根据曲线控制有功出力,同时根据AVC策略进行风电机组无功出力的调整和升压站的无功补偿装置的调节,实现从省调到风电场的系统级控制。该系统可以实现风电场的可观与可控,充分利用电网接纳风电的能力,同时还能减轻调度人员的工作量。