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Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM2.5 was 1.73 μg/m3 for the LSTM model and in the range of 2.20-4.71 μg/m3 for the other models when regulatory ambient PM2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM2.5 information, the developed models still demonstrated relatively high skill in predicting the PM2.5 levels in well-mixed indoor air.

This paper presents a DC microgrid semi-decentralized control strategy for electric vehicle fast charging and hydrogen production. The equivalent circuit models of the photovoltaics (PV), battery energy storage system and the proton exchange membrane water electrolyzer (PEMWE) are built and integrated into an equivalent bus capacity model of the microgrid, where both model accuracy and computation efficiency are promised. The microgrid, excepted for the PEMWE, is always maintained under the decentralized mode, which is based on the Virtual battery model and Bus-signaling control. The control of the PEMWE is divided into the decentralized mode and power-based mode, the mode choice of which is determined by the PEMWE controller according to time period when the price of the grid is the lowest of the day. The PEMWE could utilize the extra energy generated by the PV array to stabilize the bus voltage, the daily production target could also be satisfied. The proposed semi- empirical approach is verified in the MATLAB/Simulink environment and the feasibility and priority of which is proved.

Optimizing the size of photovoltaic (PV) and energy storage system (ESS) is a key issue to ensure reliable, resilient and economic operation of the microgrid (MG). However, multiplicity of electric vehicle (EV) load profiles and computational complexity of the optimizing models limit the application of existing methods upon restricted scenarios. In this paper, a novel framework combining physical-economical model (PEM) and data-driven model (DDM) is established. First, a data augmentation approach based on reversed Monte Carlo (RMC) is proposed to generate comprehensive synthetic EV load datasets for big data training. Subsequently, a PEM is given to determine the capacity configuration of PV and ESS under each load condition, with the objective to obtain the best financial profitability. The model takes into consideration battery degradation and optimal power dispatch. Finally, a DDM is trained with machine learning techniques to relate optimal sizes with input of EV profiles and MG features. Testing results illustrate that in the test sets, the neural network could predict the optimal configuration within 15% and 20% relative error, respectively, enormously narrowing design space of PEM, and thus produces over 88% runtime savings while maintaining high fidelity and optimality.

This paper presents a novel dual-layer framework to find the most economic photovoltaic and battery capacity combination of a grid-connected DC microgrid. The system cost during the whole lifetime is addressed in the optimization framework through the net present cost based objective function, where the investment cost, battery replacement cost and operating and management cost are taken into account. The proposed CAPN model, which combines all of the battery ageing stressing factors, is incorporated to estimate the replacement number of the battery. A particle swarm optimization algorithm, which is initialized with a rule-based power dispatch strategy, is put forward in the energy management level to achieve the optimal power dispatch. In order to adapt to variant operating conditions, the ambient and electric vehicle charging profiles throughout a whole year are employed as the input conditions of the optimization problem. A 61.8k RMB reduction is realized with the optimal configuration under the proposed method compared with the rule-based energy management strategy, indicating the superior of the proposed optimization methodology. Besides, the optimal power dispatch is also implemented with the extra power fed back to the utility grid.

The installation of rooftop photovoltaic (PV) arrays is increasing throughout the US. Until recently, pedestal type PV framing systems for rooftops were basically designed using procedures from the ASCE7-10 Components and Cladding Standard for rooftop equipment. The 2011 Japanese Standard Load design guide on structures for photovoltaic arrays was useful in characterizing the pressure coefficients on rooftops, but the Standard employs different wind speed and importance factors, making its use in the US quite limited. Even the updated 2017 version is written for a different audience. Because rooftop pressure loadings are high due to flow separation, SEAOC and other organizations contracted boundary layer wind tunnel tests of panels attached to rooftops to ascertain if the ASCE7-10 equipment loadings were appropriate. The investigations resulted in new standards for pedestal-style arrays that appear in Chapter 29 of ASCE7-16. However, the new standards are limited to simple geometries and orientations, and the dynamics of the simply-supported thin PV plates do not appear to be considered. Questions regarding the ability of the boundary tunnels to simulate accurately the turbulence at the scale required for the attached panels have been raised. In response, very limited full-scale investigations in large-scale tunnels and in situ have been undertaken to calibrate the tunnel results. The results of this paper represent one of these calibration investigations. Specifically, in situ full-scale net wind pressure loadings on a rooftop PV array in a pedestal-style framing system located on the three story Hogue Technology Building of Central Washington University (CWU) in Ellensburg, Washington were measured. The CWU campus has a rural setting in a region with steady winds: Ellensburg is located in the Kittitas Breezeway portion of the Northwest wind power region. Indeed, the Wild Horse Wind and Solar Farm is located on the outskirts of town. The data described here were collected from April through August 2014. The measured net pressure coefficient time series were similar to those for rooftop pressure loadings for low-rise buildings described in the literature such as the Wind Engineering Research Field Laboratory at Texas Tech University (Ham and Bienkiewicz, 1998 [1] ; Levitan and Mehta, 1992 [2] ). The analysis of the net pressure time series data included an examination of the minimum, maximum, mean, and RMS values. Preliminary results suggest that the range of the values is larger than assumed in the ASCE7 Standard, and that the magnitude of the loadings vary considerably spatially over the multiple panel array. The pressure loading measurements are ongoing.

With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the “virtual battery” algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries.

Plug load monitoring and associated occupant behavior interventions can play a critical role in reducing commercial building energy consumption. This study investigates whether the reduction in building energy consumption justify the added cost of plug load monitoring and occupant energy saving interventions. The objective of this study is to conduct deterministic and probabilistic return-on-investment (ROI) analysis of instrumenting workspaces, monitoring plug load usage, and applying interventions to promote building energy reduction. The study uses the findings of actual occupant energy saving intervention investigations conducted with city and federal government offices in which the association between occupant energy savings interventions and energy use risk was evaluated. While the deterministic approach led to a positive net present value, the interventions failed to recapture the initial investment, and operational expenses given the uncertainties in the estimate of costs and energy use. The mean ten-year net present value was −$3914 at a 6% discount rate considering all U.S. states. From the project manager’s perspective, other non-energy benefits can justify the additional resources.

Healthy building design guides are cogent and necessary. While elements that contribute to healthy buildings are multifactorial, the perception of sound versus noise is subjective and difficult to operationalize. To inform the commissioning process, the acoustics in an open office was examined following the first international building certification system that focuses on the well-being of occupants. Results highlight the role facility managers play in ensuring acoustical quality and offer suggestions to optimize healthy building rating systems. Mixed empirical evidence concerning the advantages of open office designs exists, as does evidence that noise, and a lack of privacy, affects workers&rsquo, levels of distraction and dissatisfaction. Sound masking systems can lower stress levels and augment performance. However, the sound produced by these systems can also be disruptive, conflicting information exists for facility managers to use when making decisions. The results suggest that, although objective measurements and healthy building guidelines for designing satisfactory indoor acoustic environments are important, changes to the physical environment, and acoustical systems, in particular, require iterative subjective assessments within the retrofit process to bolster occupant satisfaction. Mixed-methodologies used in this study may aid facilities managers in capturing and interpreting occupant data about physical stimuli in the workplace and improving the commissioning process.

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.

Fragility functions are used to represent the probability of failure of a structure or lifeline system conditional upon a hazard or set of hazards and are essential in the performance-based design process. Continuous lognormal damage fragilities are traditional, but recent formulations have implemented logit transformations from the family of generalized linear models for categorical data with a binary outcome (e.g., failure, no failure). In wind engineering, single hazard parameters derived from correlated variables (e.g., integrated kinetic energy, IKE) have been employed to indirectly include the effect of more than one hazard variable; however, even with more general hazard metrics, the lognormal formulation is still unduly restrictive for realistic fragility modeling. Using a statistical approach based on a logit formulation, a shift towards more robust fragility functions can be achieved. Because of its simplicity and ability to represent multiple predictor variables to improve the fitted model, this paper proposes use of the logit formulation of the fragility function at the system level for two or more simultaneous weather hazards. Successful applications of the model to characterize lifeline system-level fragility functions for electric power delivery during Hurricane Isaac in Louisiana and Hurricane Sandy in New York City using in-situ damage and hazard data are shown. While the results here are empirically derived, the modeling approach may be expanded for other structural systems subject to multiple loadings or demand variables.

DC microgrid is supposed to be a feasible solution to reduce the negative impact of electric vehicle (EV) fast charging on the electric grid and improve the penetration of photovoltaics (PV) generation. In this paper, an improved decentralized Virtual-battery based droop control with the capability of bus voltage maintenance, load power dispatch and SOC balance of the energy storage system (ESS) is proposed to ensure the autonomous and stable operation of the DC microgrid. The reference output voltage and virtual resistance in the droop control loop are altered dynamically based on the Virtual-battery model of the ESS. The coordinated control among the PV-ESS-Grid integrated system is realized through the primary Bus-Signaling control, where the reference voltages at which the control modes of the PV array and the grid are switched are designed based on the VirtualOCV of the ESS. The effectiveness of the proposed control strategy is validated in MATLAB/Simulink environment with an equivalent bus capacitance-based model where the EV charging profile is obtained from real-world charging data of a fast charging station. The merits of the control strategy including higher PV utilization, less frequent connection of the grid and more precise voltage tracking are highlighted in comparison with the conventional droop control strategy. Finally, the sizing of the ESSs is optimized based on the total cost of the DC microgrid, including the daily electricity cost purchased from the grid and the depreciation cost of the ESSs based on the expanded capacity degradation model of Li-ion batteries.

Recent disruptions of communities due to natural hazard events such as hurricanes and earthquakes have led to increased calls for improved resiliency of the built environment. The “built environment” denotes constructed facilities such as buildings and bridges, as well as infrastructure systems such as power delivery, transportation roadways, and water utilities. “Resiliency” is defined here as the “recovery and adaptability” during and after events which disrupt civil infrastructure services. In the context of this paper, the critically important service is energy delivery, on which many other services such as communications and transportation networks depend. The robustness of the building energy supply can be significantly enhanced through on-site renewable sources such as photovoltaic panels coupled with storage batteries. The degree to which the energy demand is met by the on-site capacity in the future will be determined largely upon advances in renewable energy generation and storage as well as in efficiency gains for commonly used equipment and appliances such as lighting fixtures and cooling systems. In this paper, we propose an improved design approach for the energy capacity of existing and new buildings as part of a greater regional community in which the total energy capacity requirements are met through increasingly enhanced on-site permanent power links, as opposed to increased reliance on the existing power grid. The metrics for characterizing resiliency will be “robustness,” “redundancy,” “resourcefulness,” and “rapidity,” with the associated metrics for sustainability being self-reliance and intergenerational equity enhancement.

According to NOAA, eleven billion-dollar weather disasters occurred in the United States in 2012. One of the predominant reasons that storms become disasters is the failure of civil infrastructure systems, also known as “lifelines,” on which communities rely. The approach to modeling resilience herein is through the use of systems-based models of performance. The models are based on empirical evidence of the ability of the underlying structural delivery system to provide essential infrastructure services to the community. For example, electric power delivery performance, subject to extreme storms, is sensitive to the combined system of mounted electrical and mechanical equipment, substations, and poles and towers that comprise the distribution and transmission systems. Interdependency is assessed first through previously derived input-output models of recovery to storm hazards and then significantly expanded in this paper. Further, the research results, based upon examination of extensive geocoded...