不确定环境下智能大厦空调系统调度策略评估

近年来,智能大厦的概念在国内外受到了高度的关注.相比于传统的建筑,智能大厦更加节能、舒适、易维护,已成为未来建筑的发展趋势.作为智能大厦空调通风系统的关键部分,空调系统及其调度策略决定了大厦整体的节能效果以及大厦中用户的舒适度.然而由于智能大厦所处的环境具有许多不确定因素,这极大增加了空调系统调度策略设计与评估的复杂程度.因此如何设计与评估不确定环境下空调系统的调度策略成为了智能大厦设计者面临的一大挑战.已有的方法主要针对智能大厦空调系统进行能耗与性能等方面的分析,但尚未有方法针对调度策略本身进行分析与评估.提出了一种基于价格时间自动机的调度策略评估框架,支持对不确定环境下的智能大厦进行精确建模与定量评估.该框架使用UPPAAL-SMC作为属性查询引擎对模型进行随机模拟运行,根据模拟结果对不同调度策略下大厦的能耗及用户的舒适度进行定量分析.实验结果表明,该方法能有效地帮助设计者进行策略的制定和选取.     

An augmented multi-objective particle swarm optimizer for building cluster operation decisions

It is envisioned that other than the grid-building communication, the smart buildings could potentially treat connected neighborhood buildings as a local buffer thus forming a local area energy network through the smart grid. As the hardware technology is in place, what is needed is an intelligent algorithm that coordinates a cluster of buildings to obtain Pareto decisions on short time scale operations. Research has proposed a memetic algorithm (MA) based framework for building cluster operation decisions and it demonstrated the framework is capable of deriving the Pareto solutions on an 8-h operation horizon and reducing overall energy costs. While successful, the memetic algorithm is computational expensive which limits its application to building operation decisions on an hourly time scale. To address this challenge, we propose a particle swarm framework, termed augmented multi-objective particle swarm optimization (AMOPSO). The performance of the proposed AMOPSO in terms of solution quality and convergence speed is improved via the fusion of multiple search methods. Extensive experiments are conducted to compare the proposed AMOPSO with nine multi-objective PSO algorithms (MOPSOs) and multi-objective evolutionary algorithms (MOEAs) collected from the literature. Results demonstrate that AMOPSO outperforms the nine state-of-the-art MOPSOs and MOEAs in terms of epsilon, spread, and hypervolume indicator. A building cluster case is then studied to show that the AMOPSO based decision framework is able to make hourly based operation decisions which could significantly improve energy efficiency and achieve more energy cost savings for the smart buildings.     

Identification of a control-oriented energy model for a system of fan coil units

In recent years, application of advanced control, fault detection and diagnosis algorithms for building heating and cooling systems has been intensively investigated with the aim to improve their energy efficiency and bring the buildings sector into the smart city arena. Hindering the trend, hysteresis and proportional–integral–derivative controllers are still a common practice for temperature control in buildings with Fan Coil Units (FCUs). Introduction of more sophisticated controllers for additional savings requires a cost-effective approach for identification of an energy model which accurately resembles thermal and hydraulic performance of a system of FCUs. In the present work, the control-oriented energy model of a system of FCUs is developed and accompanied with replicable, robust and simple methodologies for its identification derived by consolidating the advantages of physical modelling, identification methods and manufacturer’s catalogue data. The validity of the developed approach is tested on the 248-office living-lab. The introduced simple and accurate dynamic characterization of energy transmitted from a FCU to zone air fills the gap between thermal and energy management for buildings. This enables implementation of predictive building controls and unleashes significant energy and cost-saving potentials of a smart building in a smart city.     

分布式电源接入场景下的电网振荡攻击建模与检测

随着分布式电源在电网中所占比重的不断提升,针对分布式电源的攻击将给电网带来更严重的安全威胁。攻击者可以通过网络入侵手段协同控制电网中防御较弱的配网侧分布式电源功率输出,最终影响发电侧发电机等关键设备的安全运行。为保障电网安全稳定运行,亟需研究针对分布式电源接入场景下的安全威胁及其防御措施。首先,本文在电力系统动态模型基础之上建立了电网振荡攻击的最小代价攻击模型,通过协同控制多个分布式电源的功率,在牺牲最少被控节点的前提下导致电网发生振荡。其次,针对现有振荡检测算法的不足,本文提出一种启发式的攻击源检测算法,通过分析系统内各节点的势能变化,可有效辅助定位攻击源。算例仿真分析结果验证了通过最小代价攻击影响电网稳定运行的可行性,以及攻击检测方法的有效性。     

Solar energy assessment using remote sensing technologies

About 20% of the final energy consumed in Europe is used in buildings. The active and passive use of solar energy is an approach to reduce the fossil energy consumption and the greenhouse gas emissions originated by buildings. Consideration of solar energy technologies in urban planning demands accurate information of the available solar resources. This can be achieved by the use of remote sensing data from geostationary satellites which show a very high spatial and a sufficient temporal resolution compared to ground station data. This paper gives a brief introduction to the HELIOSAT method applied to derive surface solar irradiance from satellite images and shows examples of applications: The use of daylight in buildings, the generation of correlated time series of solar irradiance and temperature as input data for simulations of solar energy systems and a short-term forecast of solar irradiance which can be used in intelligent building control techniques. Finally an outlook is given on potential improvements expected from the next generation of European meteorological satellites Meteosat Second Generation (MSG).     

Leveraging operational technology and the Internet of things to attack smart buildings

In recent years, the buildings where we spend most part of our life are rapidly evolving. They are becoming fully automated environments where energy consumption, access control, heating and many other subsystems are all integrated within a single system commonly referred to as smart building (SB). To support the growing complexity of building operations, building automation systems (BAS) powering SBs are integrating consumer range Internet of things (IoT) devices such as IP cameras alongside with operational technology (OT) controllers and actuators. However, these changes pose important cybersecurity concerns since the attack surface is larger, attack vectors are increasing and attacks can potentially harm building occupants. In this paper, we analyze the threat landscape of BASs by focusing on subsystems which are strongly affected by the advent of IoT devices such as video surveillance systems and smart lightining. We demonstrate how BAS operation can be disrupted by simple attacks to widely used network protocols. Furthermore, using both known and 0-day vulnerabilities reported in the paper and previously disclosed, we present the first (at our knowledge) BAS-specific malware which is able to persist within the BAS network by leveraging both OT and IoT devices connected to the BAS. Our research highlights how BAS networks can be considered as critical as industrial control systems and security concerns in BASs deserve more attention from both industrial and scientific communities. Even within a simulated environment, our proof-of-concept attacks were carried out with relative ease and a limited amount of budget and resources. Therefore, we believe that well-funded attack groups will increasingly shift their focus towards BASs with the potential of impacting the live of thousands of people.

     

Big data: the key to energy efficiency in smart buildings

Due to the high impact that energy consumption by buildings has at global scale, energy-efficient buildings to reduce \(\mathrm{CO}_2\) emissions and energy consumption are needed. In this work we present a novel approach to energy saving in buildings through the identification of the relevant parameters and the application of Soft Computing techniques to generate predictive models of energy consumption in buildings. Using such models it is possible to define strategies for optimizing the day-to-day energy consumption of buildings. To verify the feasibility of this proposal, we apply our approach to a reference building for which we have contextual data from a complete year of monitoring. First, we characterize the building in terms of its contextual features and energy consumption, and then select the most appropriate techniques to generate the most accurate model of our reference building charged with estimating the energy consumption, given a concrete set of inputs. Finally, considering the energy usage profile of the building, we propose specific control actions and strategies to save energy.     

Rule-based system to detect energy efficiency anomalies in smart buildings,a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings.     

社区产消者能量分享研究综述

随着可再生能源和智能电网技术的发展,能源产消者作为一类新型终端用户,已在能源优化与管理方面表现出更为主动灵活的作用,对提高社区能源效率、提升能源经济性和改善本地配电网稳定性具有重要影响.首先,总结了常见的社区能源产消者类型及其特点,指出其在智能电网需求侧实现能源优化的灵活性和潜在价值;其次,剖析了社区能量分享的典型模式,归纳了各自的基本特征、优势与局限性;然后,在此基础上探讨了社区产消者能量分享涉及的能源数据预测方法、博弈问题均衡分析及分布式优化算法;最后,对社区产消者能量分享的前瞻性难点问题进行了展望,以期为相关研究提供参考.     

数据仓库在城市公共建筑能耗管理中的应用

当前众多城市公共建筑能耗监测系统中已收集了大量的建筑能耗数据。针对这些数据源存在的各自独立而且分散,不能够提供全局的数据分析环境,不能够有效支持建筑能耗的评估与建筑节能的研究等问题,提出将数据仓库技术应用于城市公共建筑能耗监管系统的解决方法。通过对建筑能耗监测系统的研究,以及对建筑能耗管理的应用需求的调研,建立城市级公共建筑能耗数据仓库的多维数据模型,对主题设计、指标设计和维度模型设计进行了探讨,并在实验阶段已成功构建了某高校公共建筑能耗数据仓库的实例。实验结果表明,该方法能够有效地为建筑能耗的管理与决策提供良好的数据分析环境。     

Modelling energy storage systems using Fourier analysis: An application for smart grids optimal management

In this paper, a new and efficient model for variables representation, named F-coding, in optimal power dispatch problems for smart electrical distribution grids is proposed. In particular, an application devoted to optimal energy dispatch of Distributed Energy Resources including ideal storage devices is here considered. Electrical energy storage systems, such as any other component that must meet an integral capacity constraint in optimal dispatch problems, have to show the same energy level at the beginning and at the end of the considered timeframe for operation. The use of zero-integral functions, such as sinusoidal functions, for the synthesis of the charge and discharge course of batteries is thus consequential. The issue is common to many other engineering problems, such as any dispatch problem where resources must be allocated within a given amount in a considered timeframe. Many authors have proposed different methods to deal with such integral constraints in the literature on smart grids management, but all of them do not seem very efficient. The paper is organized as follows. First, the state of the art on the optimal management problem is outlined with special attention to treatment of integral constraints, then the proposed new model for variables representation is described. Finally, the multiobjective optimization method and its application to the optimal dispatch problem considering different variables representations are considered.     

Energy load superposition and spatial optimization in urban design: A case study

Building energy consumption accounts for a large portion of total energy-use in a city or a regional district. However, energy load spatial distribution has seldom been considered during urban design phase. And energy conservation and energy efficiency measures pay more attention to individual building than buildings in a district or regional space as a whole. If buildings with different functions are mixed together and share same energy system, the savings on system capacity and peak electricity load can be significant. In this paper, a load superposition concept is proposed. The term ‘superposition’ refers to overlapping of energy demand load curves from different buildings and so that the total peak is smaller than the sum of individual peaks. Three spatial optimization methods of demand side load management and three different schemes of energy systems are proposed in this paper. And economic analysis is recommended to evaluate the different energy systems. The applicability of different approaches and the significance of load superposition was analyzed and elaborated through a case study to offer planners a feasible way for evaluating the potential of load spatial optimization.     

Nonlinear model reduction and model predictive control of residential buildings with energy recovery

Residential and commercial buildings account for a significant portion of the electricity consumed in the United States. Their operation is subject to fluctuations in weather and occupancy which, in turn, are reflected in large variations in the load that buildings impose on the grid during the day and at night time. In view of mitigating such fluctuations (and their broader impact on energy generation), understanding the dynamic behavior of buildings and a focus on energy management (rather than simply temperature control), is essential. In this paper, we begin by analyzing building dynamics and use singular perturbation arguments to provide a theoretical justification for the empirically acknowledged multiple time scale dynamic response of buildings. We also derive reduced-order models for the dynamics in each time scale for a prototype residential building. Our analysis accounts for the potential use of heat recovery ventilators (HRVs), and we show that the presence of energy recovery leads to the emergence of a dynamic behavior with three time scales, including an overall, system-wide component which involves both the building and the HVAC system. We use our dynamic results to formulate a set of synthesis guidelines for control systems addressing either temperature regulation or geared towards minimizing operating cost. A detailed simulation case study demonstrates the application of the derived reduced-order models in the design of a nonlinear predictive model-based optimal energy management strategy for a model of a single-zone test building situated on the University of Texas campus. The proposed controller exhibits excellent performance, can easily be executed in real-time and has the capability to shift peak loads as part of a demand flattening strategy.     

The social smart grid: Dealing with constrained energy resources through social coordination

The smart grid promises to improve the efficiency and reliability of tomorrow's energy supply. One of the biggest achievements of future smart grids will be their distributed mode of operation which effectively eliminates vulnerable nodes causing single points of failures in the grid. However, due to the lack of centralized energy production and control, the coordination of energy consumption becomes first priority. Because there do not exist technologies to store energy at large-scale yet, all energy that is required must be produced at the same time. The biggest challenge of energy producers is therefore to reliably predict and provide the right amount of required energy to avoid shortages and breakdowns. In this paper, we propose a novel way to let smart grid stakeholders, i.e., energy producers and consumers, coordinate their energy demands themselves. For that purpose we combine traditional social network models and service-oriented computing concepts with the smart grid to allow consumers to form communities according to their energy consumption behavior. These communities enable them to interact with other grid stakeholders to coordinate energy consumption plans and set up private energy sharing alliances. This way, the utility provider and industrial energy producers can rely on a better predictable and a smoother energy demand of customers. We introduce a software framework, making use of widely adopted standards, demonstrate its feasibility with an agent-based simulation, and discuss its overall applicability.     

智慧能源-----人工智能技术在电力系统中的应用与展望

在环境污染日趋严重,化石能源逐渐枯竭的背景下,能源系统的发展趋向于清洁化、智能化,我国已将智慧能源的发展提升为国家战略.电力系统作为能源系统的核心环节,应用广泛,具有较强的调节能力且控制复杂,其智能化程度将决定能源系统的智能化水平.伴随着分布式电源、电动汽车、分布式储能元件等具有能源生产、存储、消费多种特性的新型能源终端高比例接入电网,现代电力系统呈现出复杂非线性、强不确定性、强耦合性等特点,传统建模、优化、控制技术存在诸多局限性,人工智能技术将是解决复杂系统控制与决策问题的有效措施.鉴于此,首先梳理人工智能在电力系统应用的发展脉络;然后根据人工智能在电力系统的应用热点领域,阐述人工智能技术在电力系统调度、规划以及电力市场等方面的应用,并对各重点研究内容的未来方向进行展望.     

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs.     

多台冷水机组联合运行优化控制策略

冷水机组通常占建筑物系统总能耗的主要部分,因此冷水机组运行数量控制在实现空调系统节能方面起着重要作用。针对多台冷水机组联合运行时台数选择和负荷分配不合理的问题,文章将通过TRNSYS模拟得出的负荷值聚类分析,确定不同负荷值对应的冷水机组运行台数,并提出一种冷水机组排序优化控制方法改善顺序控制方式以实现其合理运行与节能。以某大型办公建筑为例,将优化控制前后冷水机组运行的进行总能耗对比。实验结果表明:与顺序控制相比,冷水机组优化控制后两个工作日总能耗分别节约126.1kW和342.5 kW,节能率分别为4.15%和5.22%。通过利用冷水机组顺序优化控制可满足建筑的冷负荷需求同时实现降低系统能耗,在空调系统节能方面具有工程实际应用价值。     

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to consumer preferences while planning the operation of appliances. A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes. Furthermore, the allotment mechanism has been designed to manage the grid appliances through the edge node. The proposed model helps to improve the latency in the grid appliances scheduling process.     

基于深度学习的智能楼宇微网负荷预测方法研究

楼宇微网是智能电网的重要组成部分,提高智能楼宇微网负荷预测精度,有助于对楼宇能效系统进行优化控制和调度规划。针对智能楼宇微网用电负荷数据的特点,提出了基于深度学习的智能楼宇微网短期负荷预测模型。首先用无监督的贪心算法对原始数据进行负荷数据的特征学习,完成对智能楼宇微网负荷数据的特征提取;然后挖掘智能楼宇微网负荷数据间的相互关系;最后用反向传播算法微调整个模型的参数。实验结果表明,提出的预测模型与传统预测模型相比具有更高的预测精度,且具有很好的可行性和有效性。     

Model reduction and model predictive control of energy-efficient buildings for electrical heating load shifting

In France, buildings account for a significant portion of the electricity consumption (around 68%), due to an important use of electrical heating systems. This results in high peak load in winter and causes tensions on the production-consumption balance. In view of reducing such fluctuations, advanced control systems (including the Model Predictive Control framework) have been developed to shift heating load while maintaining indoor comfort and taking advantage of the building thermal mass. In this paper, a framework for developing optimisation-based control strategies to shift the heating load in buildings is introduced. The balanced truncation method and a time-continuous optimisation method were used to develop a real-time control of the heating power. These two methods are well suited for control problems and yield precise results. The novelty of the approach is to use reduced models derived from advanced building simulation software. A simulation case study demonstrates the controller performance in the synthesis of a predictive model-based optimal energy management strategy for a single-zone test building of the “INCAS” platform built in Le Bourget-du-Lac, France, by the National Solar Energy Institute (INES). The controller exhibits excellent performance, reaching between 6 and 13% cost reduction, and can easily be applied in real-time.