Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties

It is acknow ledged that the conventional design methods can easily lead to oversized system or unsatisfactory performance for different design conditions. M ost existing studies on design optimization of net zero energy building( nZ EB) are conducted based on deterministic data / information. How ever,the question is: How is the actual performance of a design nZ EB in different years considering uncertainties? This study,therefore,proposed a robust design method for sizing renew able energy systems in nZ EB concerning uncertainties in renew able resources and demand load. The proposed robust design method is applied tothe planning of renew able energy system for the Hong Kong Zero Carbon Building. The annual performance of nZ EB under the optimal design options are systematically investigated and compared using the proposed robust design method and the deterministic method. It is meaningful to obtain a fitting formula to identify the relationship betw een the probability of achieving annual zero energy balance and the design mismatch ratio. On the basis of M onte Carlo uncertainty propagation methods, the uncertainty of nZ EB performance is quantified w hich provides flexibility for designers in selecting appropriate design options according to the required probability of achieving nZ EB during the design stage.     

Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements

Near-zero energy buildings( nZEBs) are considered as an effective solution to mitigating CO_2 emissions and reducing the energy usage in the building sector. A proper sizing of the nZEB systems( e. g. HVAC systems,energy supply systems,energy storage systems, etc.) is essential for achieving the desired annual energy balance,thermal comfort,and grid independence. Two significant factors affecting the sizing of nZEB systems are the uncertainties confronted by the building usage condition and weather condition,and the degradation effects in nZEB system components. The former factor has been studied by many researchers; however,the impact of degradation is still neglected in most studies. Degradation is prevalent in energy components of nZEB and inevitably leads to the deterioration of nZEB life-cycle performance. As a result,neglecting the degradation effects may lead to a system design which can only achieve the desired performance at the beginning several years. This paper,therefore,proposes a life-cycle performance analysis( LCPA) method for investigating the impact of degradation on the longitudinal performance of the nZEBs. The method not only integrates the uncertainties in predicting building thermal load and weather condition,but also considers the degradation in the nZEB systems. Based on the proposed LCPA method,a two-stage method is proposed to improve the sizing of the nZEB systems.The study can improve the designers "understanding of the components"degradation impacts and the proposed method is effective in the life-cycle performance analysis and improvements of nZEBs. It is the first time that the impacts of degradation and uncertainties on nZEB LCP are analysed. Case studies showthat an nZEB might not fulfil its definition at all after some years due to component degradation,while the proposed two-stage design method can effectively alleviate this problem.     

Cost optimal and nearly zero (nZEB) energy performance calculations for residential buildings with REHVA definition for nZEB national implementation

This study determined cost optimal and nearly zero energy building (nZEB) energy performance levels following the REHVA definition and energy calculation methodology for nZEB national implementation. Cost optimal performance levels – meaning the energy performance leading to minimum life cycle cost – were calculated with net present value method according to the cost optimal draft regulation. The seven-step procedure was developed to conduct cost optimal and nZEB energy performance levels calculations in systematic and robust scientific fashion. It was shown that cost optimal primary energy use can be calculated with limited number of energy simulations as only four construction concepts were simulated and cost calculated. The procedure includes the specification of building envelope components based on specific heat loss coefficient and systems calculation with post processing of energy simulation results, without the need to use iterative approach or optimization algorithm. Model calculations were conducted for Estonian reference detached house to analyse the difference between the cost optimal and nZEB energy performance levels. Cost optimal energy performance level of Estonian reference detached house was 110 kW h/(m² a) primary energy including all energy use with domestic appliances and it was significantly lower than the current minimum requirement of 180 kW h/(m² a).     

基于子集模拟的边坡可靠度分析方法研究

边坡稳定受到诸多不确定性因素的影响,比如土体性质的空间变异性以及地层情况的不确定性。这些不确定性因素的影响可以通过蒙特卡洛模拟(Monte Carlo Simulation, MCS)进行定量地分析。MCS概念简单并具有广泛的适用性。但是,在小概率失效区域内,MCS计算效率很低,需要庞大的随机样本量来保证一定的计算精度。本文提出了一种实用的边坡可靠度分析方法。通过采用一种高级的MCS方法(Subset Simulation, 子集模拟)来提高小概率区域内的计算效率以及计算精度,并以EXCEL的表单环境为平台,联合使用Visual Basic Application(VBA)编写计算程序。在该程序中,子集模拟、边坡稳定的确定性分析和不确定性分析分别由三个相对独立的计算模块实现。最后,本文以James Bay 土坝为例,简明地说明了所提出方法的有效性,并探索了临界滑动面的不确定性对边坡可靠度分析的影响。     

Risk-based group decision making regarding renewable energy schemes using a stochastic graphical matrix model

Improvements to sustainability are generally measurable based on their environmental, economic, and socio-cultural effects. This study applied this concept by developing and empirically testing a risk-based method for assessing renewable energy policy. An integrated theoretical framework is proposed for analyzing group decision-making regarding renewable energy (RE) policy selection. The proposed graphical matrix approach combined with Monte Carlo simulation compares alternative RE schemes by identifying and measuring critical performance indicators with acceptable reliability. The mathematical model reliably prioritizes alternatives using majority voting to address uncertainty in multi-criteria decision making process. A case study using historical data from previous RE development projects to confirm the feasibility of this approach. Compared to the conventional deterministic method, the stochastic graphical matrix approach provides more reliable estimation accuracy, decision quality, and efficiency in selection of sustainable renewable energy. The systematic approach provides policy makers information for use in evaluation by synthesizing the judgments of a panel of experts.     

Failure probability minimization of buildings through passive friction dampers

This paper proposes a methodology for robust optimization of the failure probability of buildings subjected to stochastic earthquakes, using a less common type of passive energy dissipation device: the friction dampers. There is a lack of studies on optimal positions and parameters of passive friction dampers, and additionally, the few studies found in the literature consider the problem in a deterministic way. The robust optimization proposed in this paper is carried out through the recently developed backtracking search optimization algorithm, which is able to deal with optimization problems involving mixed discrete (positions) and continuous (friction forces) design variables. In order to take into account uncertainties present in both the system and the dynamic excitation (earthquakes), some parameters are modeled as random variables, and consequently, the structural response becomes stochastic. For illustration purposes, a 10‐story building is analyzed. The results showed that the proposed method was able to reduce the failure probability in approximately 99% with only three friction dampers, installed in their best positions and with their optimized friction forces. The proposed methodology is quite general, and it is believed that it can be recommended as an effective tool for optimum design of friction dampers. Copyright © 2016 John Wiley & Sons, Ltd.     

Safety analysis of structures with probability and evidence theory

In safety analysis of structures, classical probabilistic analysis has been a popular approach in engineering. However, it is not always to obtain sufficient information to model all uncertain parameters of structures system by probability theory, especially at early stage of design. Under this circumstance, probability theory (used to model random uncertainty) combined with evidence theory (used to model epistemic uncertainty) may be utilized in safety analysis of structures. This paper proposed a novel method for safety analysis of structures based on probability and evidence theory. Firstly, Bayes conversion method is used as the way for precision of evidence body, and the mean and variance of epistemic uncertain variables is defined. Then epistemic uncertainty variables is transformed to normal random variables by reflection transformation method, and the checking point method (J-C method) is used to solve most probability point and reliability. A numerical example and two engineering examples are given to demonstrate the performance of the proposed method. The results show both precision and computational efficiency of the method is high. Moreover, the proposed method provides basis for reliability-based optimization with the hybrid uncertainties.     

Shape design of arch dams under load uncertainties with robust optimization

Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based design of new arch-type dams by applying means of robust design optimization (RDO). This type of optimization takes into account uncertainties in the loads and in the material properties of the structure. As classical procedures of probabilistic-based optimization under uncertainties, such as RDO and reliability-based design optimization (RBDO), are in general computationally expensive and rely on estimates of the system’s response variance, we will not follow a full-probabilistic approach but work with predefined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. As a result, robust and reliable designs are obtained and the result is independent from any assumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimization method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RDO method provides a robust solution against uncertainty. To reduce the computational cost, a ranking strategy and an approximation model are further involved to do a preliminary screening. By this means, the robust design can generate an improved arch dam structure that ensures both safety and serviceability during its lifetime.     

Robust nonlinear decision mapping approach for online bus speed control under uncertainty

The degradation of bus system attractiveness is primarily caused by low-level service quality and reliability. As an essential technology for bus operation management, online bus speed control has proven to be a flexible and effective solution to mitigate bus bunching and enhance the service level of bus operation systems. In this study, we propose a robust nonlinear decision mapping (RNDM) approach that uses real-time key bus system states to control bus speeds and accounts for uncertainties associated with passenger demands at stations and traffic speeds of interstation links. We develop this approach through a design process that involves learning the input–output mapping relation of a nonlinear programming simulation-based optimization (NLPSO) method using regression tree with AdaBoost. Critical parameters of the fitted regression tree with AdaBoost are then optimized offline using a distributionally robust simulation-based optimization (DRSO) model that is solved by a simulation-based optimization (SO) algorithm. The resulting RNDM method effectively handles two types of uncertainties, expressed by two ambiguity sets of probability distributions, and ensures good bus operation performance even under worst-case uncertainty levels. Numerical experiments reveal that the RNDM, NLPSO, and integer programming SO (IPSO) methods successfully mitigate bus bunching and improve service efficiency and robustness, compared to the no-control scenario. Furthermore, the RNDM method outperforms NLPSO and IPSO in terms of comprehensive performance under uncertainties and demonstrates practical operability. In conclusion, this study presents an innovative general framework that uses a nonlinear decision mapping optimized offline by an SO approach to address online simulation-based optimal decision-making problems under uncertainties, which can be applied to solve similar problems.     

Robust Transportation Network Design Under Demand Uncertainty

Abstract:   This article addresses the problem of a traffic network design problem (NDP) under demand uncertainty. The origin–destination trip matrices are taken as random variables with known probability distributions. Instead of finding optimal network design solutions for a given future scenario, we are concerned with solutions that are in some sense "good" for a variety of demand realizations. We introduce a definition of robustness accounting for the planner's required degree of robustness. We propose a formulation of the robust network design problem (RNDP) and develop a methodology based on genetic algorithm (GA) to solve the RNDP. The proposed model generates globally near-optimal network design solutions, f, based on the planner's input for robustness. The study makes two important contributions to the network design literature. First, robust network design solutions are significantly different from the deterministic NDPs and not accounting for them could potentially underestimate the network-wide impacts. Second, systematic evaluation of the performance of the model and solution algorithm is conducted on different test networks and budget levels to explore the efficacy of this approach. The results highlight the importance of accounting for robustness in transportation planning and the proposed approach is capable of producing high-quality solutions.     

Life cycle emissions analysis of two nZEB concepts

The net-zero emissions building (nZEB) performance is investigated for building operation (EO) and embodied emissions in materials (EE) for Norway's cold climate. nZEB concepts for new residential and office buildings are conceived in order to understand the balance and implications between operational and embodied emissions over the building's life. The main drivers for the CO₂ equivalent (CO₂e) emissions are revealed for both building concepts through a detailed emissions calculation. The influence of the CO₂e factor for electricity is emphasized and it is shown to have significant impact on the temporal evolution of the overall CO₂e emissions balance. The results show that the criterion for zero emissions in operation is easily reached for both nZEB concepts (independent of the CO₂e factor considered). Embodied emissions are significant compared to operational emissions. It was found that an overall emissions balance including both operational and embodied energy is difficult to reach and would be unobtainable in a scenario of low carbon electricity from the grid. In this particular scenario, the net balance of emissions alone is nonetheless not a sufficient performance indicator for nZEB.     

Global sensitivity analysis in wastewater treatment plant model applications: Prioritizing sources of uncertainty

This study demonstrates the usefulness of global sensitivity analysis in wastewater treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their impact on performance criteria. The study, which is performed with the Benchmark Simulation Model no. 1 plant design, complements a previous paper on input uncertainty characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R² > 0.9) for effluent concentrations, sludge production and energy demand. This high extent of linearity means that the plant performance criteria can be described as linear functions of the model inputs under the defined plant conditions. In effect, the system of coupled ordinary differential equations can be replaced by multivariate linear models, which can be used as surrogate models. The importance ranking based on the sensitivity measures demonstrates that the most influential factors involve ash content and influent inert particulate COD among others, largely responsible for the uncertainty in predicting sludge production and effluent ammonium concentration. While these results were in agreement with process knowledge, the added value is that the global sensitivity methods can quantify the contribution of the variance of significant parameters, e.g., ash content explains 70% of the variance in sludge production. Further the importance of formulating appropriate sensitivity analysis scenarios that match the purpose of the model application needs to be highlighted. Overall, the global sensitivity analysis proved a powerful tool for explaining and quantifying uncertainties as well as providing insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants.     

环境与建筑的实现 ——面向净零能耗建筑

在概述实现nZEB的必要手法的同时,详细地介绍了一个有关室内环境合理化的概念。这一概念就是：由基于可用能理论的人体可用能平衡公式引出有关舒适度的冷热感（指标）。文章在论述可用能这一理论相对于节能的重要性的同时,还表述了我们跳出思维定势获得真知,并在获得真知的基础上实现nZEB的重要性。     

低矮房屋风致结构响应极值不确定性分析

风致结构响应极值估算在结构抗风的可靠度设计中十分重要。在整个极值估算过程中,由于许多不定或随机的因素存在(如：极值自身、估算方法、样本采集、极值概率模型等),得到的极值通常存在不确定性。在各种影响因素中,该文将考虑结构响应极值变量本身的随机特性,对任意分位点处响应极值的不确定性进行分析。首先,利用有限元软件对低矮房屋模型进行框架结构设计并优化,加载风压荷载得到结构响应时程数据。然后,基于Hermite多项式模型(HPM)转换过程方法,估算得到响应的极值Ⅰ型分布(Gumbel);基于该极值估算方法,提出时程样本偏度、峰度、零超越次数与Gumbel分布两个参数之间的经验公式。接着,考虑前四阶矩的不确定性,利用经验公式以及多步概率分析,对任意分位点处响应极值的不确定性进行估计。最后,给出相关结论。     

Behavioral variables and occupancy patterns in the design and modeling of Nearly Zero Energy Buildings

The objective of obtaining high performance energy buildings can be reached considering the contemporaneous effects of technical characteristics and occupancy. Recent studies report that as buildings become more energy efficient, the behavior of occupants plays an increasing role in consumption. Therefore, a construction designed to be a Nearly Zero Energy Building (nZEB) might generate higher consumption than expected if the assumptions made in the simulation process are not respected during the real use. The occupant can modify the control strategies of internal variables (heating/cooling system operation, set point temperature, ventilation, lighting) and the users’ behavior has a high impact on the utilization of plants and equipment. A significant contribution is also represented by the internal gains that have a direct relation with occupancy. The aim of this study is to assess the influence of housing occupancy patterns on the definition of residential nZEB in Italian climatic conditions. The investigation has been carried out considering a case study consisting of a building designed according to the National Standards. Successively, different conditions of the building usage are analyzed using dynamic energy simulations that allow exploration of the different occupation modes. The variability of the family composition and the occupancy scenarios are defined based on the data collected in the specific context. The investigation provides information regarding the effects of human variables (occupants’ needs and preferences) on the final energy performance of low energy buildings and highlights the combination of variables that are important in the definition of nZEB as net zero source energy.     

Influence of Demand Uncertainty and Correlations on Traffic Predictions and Decisions

Abstract: Decisions to improve a regional transportation network are often based on predictions of future link flows that assume future travel demand is a deterministic matrix. Despite broad awareness of the uncertainties inherent in forecasts, rarely are uncertainties considered explicitly within the methodological framework due at least in part to a lack of knowledge as to how uncertainties affect the optimality of decisions. This article seeks to address this issue by presenting a new method for evaluating future travel demand uncertainty and finding an efficient technique for generating multiple realizations of demand. The proposed method employs Hypersphere Decomposition, Cholesky Decomposition, and user equilibrium traffic assignment. Numerical results suggest that neglecting correlations between the future demands of travel zone pairs can lead to improvement decisions that are less robust and could frequently rank improvements improperly. Of the six sampling techniques employed, Antithetic sampling generated travel demand realizations with the least relative bias and error.     

Energy and exergy analysis of prosumers in hybrid energy grids

Surplus energy can be a recurrent phenomenon in zero-energy buildings (ZEBs) with onsite generation systems, usually resulting in the export of excess electricity. Yet, converting electricity into heat and exporting it could improve the overall energy balance. This study analyses the energy and exergy performance of a Finnish nearly zero-energy building (nZEB) as a heat and electricity prosumer, and proposes alternative energy topologies to improve energy and exergy levels, primary energy demand and CO₂ emissions. The results show that increasing the installed capacity of the photovoltaic systems would lead to zero energy, exergy, emissions and a balance of primary energy. However, by instead using the surplus electricity to drive a heat pump and export heat, the currently installed capacity would lead to a net energy export of over 4000?kWh/a. Thus, energy conversion could significantly enhance the contribution from heat and electricity prosumers to smart energy grids, though not without affecting other criteria. Two management strategies arise: favouring heat export improves the net energy and CO₂ emissions reduction but lessens the net exergy, while favouring electricity export improves the net exergy and primary energy reduction. The findings highlight that energy conversion can enhance nZEB performance and its exchange with hybrid grids.     

Sustainability in highrise building design and construction

This paper presents a review of the recent literature on sustainability in construction and design with a focus on highrise buildings. The paper is divided into the following main sections: energy consumption, environmental effects and green practices for highrise buildings. A number of concepts in sustainable design are reviewed including passive solar design, renewable energy resources, cogeneration and tri‐generations, embodied energy reduction, net zero energy building, carbon emission reduction, envelope environment quality, green materials, efficient mechanical design and innovative structural systems. Their applications in a dozen signature and iconic structures are described. In order to achieve net zero energy in a new highrise building, first, multiple green solutions need to be evaluated using two categorical solutions: passive solar and envelop environment design and renewable energy resources along with efficient energy generators. Next, a robust optimization algorithm should be used to select the optimum set of solutions. This is worth pursuing in future sustainable design of highrise buildings because they are massive and complex structures with many components. Copyright © 2016 John Wiley & Sons, Ltd.     

Performance comparisons of two system sizing approaches for net zero energy building clusters under uncertainties

Uncertainties have significant impacts on system sizing in net zero energy building(NZEB)clusters and they have to be well considered.Through Monte Carlo simulation and statistical analysis,the impacts of three typical types of parameter uncertainties have been investigated in the study.Considering the uncertainty impacts,this study aims to compare the multi-criteria performance of two design approaches for system sizing of a NZEB cluster.The first one is the conventional separated design in which dedicated systems are separately designed in individual buildings.The second one is the integrated design in which integrated systems are designed to provide services to all buildings.The study results show that the integrated design approach can achieve significant system size reductions and large initial cost savings as compared with the conventional separated design.The initial costs of the air-conditioning,PV and wind turbine systems can be reduced by 14.4%,13.7% and 11.8%respectively.The integrated design also achieves improved grid friendliness and equivalently good indoor thermal comfort in comparison with the conventional separated design.With such improved performance,the integrated design should replace the conventional separated design for system sizing in NZEB clusters as uncertainties considered.