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School of Architecture

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College of Design

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Now showing 1 - 10 of 37

Optimizing microgrid distributed energy resources with varying building loads: Analysis and simulation

(Georgia Institute of Technology, 2018-08-09) Haroon, Sohail

As microgrids continue to evolve and become more prevalent, there arises a need to understand how best to design while addressing the fundamental objective of meeting energy loads. As a localized energy entity, a microgrid brings together distributed energy resources such as photovoltaics and energy storage systems with an array of building loads within a well-defined electrical boundary. Microgrids can vary considerably in scope, co-existing with the utility grid infrastructure, or being able to operate independently of it, or some combination in between of grid-tie and off-grid operation. Many challenges face the design and operation of a microgrid involving intelligent controllers and dispatchers, balancing generation resources, interacting with the utility grid, and doing all this in a cost-effective manner. This study examines the role of building load profiles in optimization of distributed energy resources, in particular, photovoltaics and storage system. The grid is assumed to be stable and contrasting rate structures are explored. Similarly, contrasting load profiles can shed light on a microgrid’s ability to meet demand versus energy loads. Modeling and simulation is done via an industry standard tool, HOMER GRID. Detailed hourly load profiles for various building mix profiles are generated via an expanded building energy modeling tool, Energy Performance Calculator (EPC), developed at the Georgia Institute of Technology. Demand response is also handled via EPC. Optimization is across the spectrum of net present cost, operating cost, return on investment, and a redefined levelized cost of electricity metric. A simple methodology is derived that can aid in the general design of balancing and optimizing distributed energy resources based on the findings of optimization across scenarios. Of vital importance to a microgrid stakeholder is risk mitigation in the deployment and usage of distributed energy resources, operating costs, and load fulfillment. This study paves the path of better understanding of integration of microgrids within an evolving smarter utility grid. Future studies will explore an even wider mix of buildings, the effect of electric vehicle (EV) charging stations via the building load profiles, and the evolution of microgrid rate structures from the perspective of Independent System Operators (ISO) and Regional Transmission Organizations (RTO). In addition, scope will be expanded to include microgrids that service villages and islands where grid stability cannot be assumed thus covering the gamut of microgrid presence worldwide.
The synergistic effects of thermal environment and visibility upon the popularity of street retail area: A case study of a retail arcade in Guangzhou

(Georgia Institute of Technology, 2018-08-01) Li, Yifan

For every building design process, three elements should be taken into considerations: building type, geometry and environment. These elements mutually influence one another; the aim of architectural design is to find the most appropriate combination of them. The three elements could be analyzed and modeled by using tools and methods in the fields of architecture typology, space syntax, and building performance simulation. The use of such tools supports not only qualitative research and evaluation, but also quantitative comparison. This work focuses on the arcade, a type of street retail space in South China. The overhanging 2nd floor not only moderates the thermal and environmental quality of the passage beneath it, but also affects the visibility of store fronts. In this study the conditions created in arcaded environments are compared to those in a normal street retail environment. Several analysis tools are used: Isovist and daylighting analyses are combined in order to model the visibility of store fronts; environmental simulation is used to assess thermal performance (e.g. temperature, wind speed, and humidity). The results are used to characterize the attraction of stores which are similar regarding size, location and retail type but are interfaced to different types of outdoor space. By combining the results of the analysis with observations of the stores’ popularity the research concludes with recommendations about the design of store environments that are more likely to attract visitors. Tools: Depthmap, Grasshopper, Diva, Ladybug, Honeybee, IES-VE
Integrated assessment of buildings and distributed energy resources (DER) at the neighborhood scale

(Georgia Institute of Technology, 2017-11-13) Carneiro, Gustavo Antonio

In urban regions, traditionally a main electric grid fed by centralized power plants serves the growing energy demand of residential and commercial buildings. However, the advent of new technologies, such as distributed renewable energy generation, local energy storage, and smart controls, is transforming the way buildings interact and transact with the electric grid. When operating in coordination, several buildings or households can leverage their aggregate potential and use their energy flexibility and distributed resources to improve the operation of both the main grid and the pool of integrated and intelligent buildings. Much attention has been drawn to the potential benefits of these types of integration, especially the capabilities they can provide in terms of aggregate demand management and local power resilience. Nevertheless, building energy modeling at the urban level has not yet reached the necessary computational manageability and simulation robustness to assess these novel scenarios. To address this hiatus, the current thesis presents a computer-aided energy simulation method to model the integration of multiple buildings and distributed energy resources (DER) at the neighborhood scale. The proposed methodology uses a reduced order simulation approach to achieve a reliable and tractable dynamic modeling framework that can manage multiple transacting building energy models and DER models in a single platform. To test the modeling approach, this study first carries out a virtual experiment of a small community in Miami, FL, where it is possible to compare the outcomes of community energy consumption from our reduced order model to the outcomes from a higher order simulation approach. When using the community energy model to evaluate the performance of different DER options for community peak load shaving, we can observe that the influence of the model order reduction reveals to be very minor when compared to other uncertainties related to scenario variability and, especially, systems’ efficiencies. Secondly, we apply the reduced order modeling approach to an existing residential community in Rancho Cordova (Sacramento County), CA, with solar energy generation and battery energy storage. With this case study, we demonstrate the viability of our approach to construct and calibrate a reduced order model of fifteen households based only on limited and general data related to energy performance of the entire neighborhood. The developed reduced order model is used to evaluate the performance of different energy storage arrangements for reducing the occurrence of community super peak loads. In this virtual experiment, we can demonstrate how the model allows for uncertainty analyses over the influence of input parameters, as well as for more sophisticated optimization studies, including stochastic optimization, in a timely and transparent fashion. Finally, the proposed reduced order simulation approach is used to construct and test relevant energy performance measures at the neighborhood scale. Using the model unique features of manageability, reliability and flexibility, we propose the foundations for quantifying and measuring “community energy resilience” for outage situations, based on concepts of number of sustained hours and respective energy end-use convenience levels. We also measure and monetize DER options for providing “community energy flexibility”, aimed at shaping the load profile of a residential community to match the electric grid needs.
Addressing data informativeness in risk-conscious building performance simulation applications

(Georgia Institute of Technology, 2017-08-02) Li, Qi

Building performance management remains an important aspect in reducing building energy consumption and enhancing occupants’ thermal comfort and work productivity. Recent decades witnessed the maturity and proliferation of numerous methods, software and tools that span the whole spectrum of common building performance management practice. Among those related research and applications, the use of information and data in calibration and validation of building performance simulation (BPS) models constitutes an important subject of study especially in fault detection, operations management, and retrofit analysis. An extensive review of BPS model calibration and validation studies reveals two major research gaps. First, contemporary model calibration practice calls for an effective and robust method that can systematically incorporate a variety of information and data, handle modelling and prediction uncertainties, and maintain consistent model performance. Second, current approaches to collecting information and data in real practice largely depend on individual experience or common practice; further study is needed to understand the value of information and data, i.e. assess data informativeness, such as to support specific decision-making processes in choosing data monitoring strategies and to avoid missed opportunities or wasted resources. To this end, this dissertation develops a new framework to address data informativeness in model calibration and validation to answer two major research questions: 1) how to make optimal use of available information and data to calibrate a building simulation model under uncertainty, and 2) how to quantify the informativeness of information and data for risk-conscious building performance simulation applications. This framework builds upon uncertainty propagation using detailed measurements, and inverse modelling using Bayesian inference. It introduces probabilistic performance metrics to assess model prediction consistency and quantify data informativeness. Following an explanation of the framework’s theoretical soundness, this dissertation provides two case studies to demonstrate its practical effectiveness. The first is a controlled experiment in the Flexlab test facility at Lawrence Berkeley lab. A new validation methodology is proposed to validate a simulation model under uncertainty, in which the validation criteria build upon the introduced probabilistic performance metrics. Given the experiment setup, uncertainty propagation based on synthetic measurements is applied, which effectively improves prediction agreement and reduces the risk of accepting invalid simulation outcomes. The second is to determine the appropriate model form and metering data for a hypothetical intervention analysis of an existing building with hydronic heating on the Cambridge, UK campus. A three-level modelling method is proposed to enable modelling all thermal processes occurring in individual rooms while efficiently modelling the whole building to estimate heating system performance. Different sets of metering data are then used to calibrate the physical model, and the result indicates the superiority of Bayesian inference in exploiting the value of data, the necessity of room temperature and electricity monitoring under uncontrolled conditions, and the potential of daily metering data for calibration in real building performance management practice.
Multi-aspect energy performance of building form in eight U.S. climate zones

(Georgia Institute of Technology, 2017-07-19) Feng, Tianyu

This research examines how building massing and building form impacts on multiple levels of building energy usage and inspects sensitivity of form parameters against other components using a building energy simulation-based framework. Based on literature review, a new concept, Relative Compactness (RC) is implemented throughout the research as the leading form characteristic to evaluate and validate the energy performance impact of building massing and form parameters. From an architectural design perspective, the RC is coupled with window sizes, window distribution and orientation; they are collectively treated as defining building form. It was found that a decrease of RC shows strong correlation with the increase of building energy usage in comparison to a cubic form for major building types located in different climate zones. In the study of the building form, a comprehensive comparison of multiple energy saving measures is conducted to rank the energy saving potentials of various parameters, include HVAC system type, cooling EER, heating COP, lighting power density, daylighting sensor, occupancy sensor, window U-value window and roof R-value, in a building energy simulation-based model. Building form impacts energy usage significantly depending on the range of the parameters defined in this study, especially the window related properties including the unit U-value, window area and distribution over different building facade orientations. Overall, the energy saving variation of all the evaluated strategies is highly interactive, and one component could affect the total energy consumption greatly. It is important to make sure each aspect of a project guarantees a proper efficiency level to maximize its effect. The results are discussed and shown to vary by climate zone.
Optimal strategies for demand charge reduction by commercial building owners

(Georgia Institute of Technology, 2017-05-23) Zhang, Yuna

A substantial part of electricity bills in various types of commercial buildings, such as office buildings, hospitals and retails can consist of demand charges. Demand charges represent the penalty for an electricity consumer levied by the utility provider. They are typically a direct result of the shape of the power duration curve, in particular, the hours that a certain power level is exceeded in a given billing period (normally a month). Lowering the peak and/or reducing the hours that a power threshold is exceeded can drastically reduce demand charges. The ability to do so by dynamic, operational adjustments reflects the “energy flexibility” of the building. This term is now widely used in Europe and is the subject of a new international effort (IEA Annex 67) in this area. This thesis targets the optimal choice among design and operational measures in a retrofit or new design project that delivers the most effective way of reducing demand charges and increasing energy flexibility of commercial buildings. This goal will be achieved through an analysis of all feasible energy and peak reduction measures in different building types and in different use contexts. A search algorithm that compares all possible interventions will deliver the optimum, first with a deterministic analysis then with the recognition of the effects of all possible sources of uncertainty. This thesis evaluates the measures that are commonly adopted to decrease energy consumption and increase energy flexibility and thus reduce demand charges, including (1) upgrading building components and installing energy efficient equipment; (2) applying dynamic building load control strategies such as demand-side management; (3) installing a rooftop photovoltaic (PV) panel array. Operational interventions include the manipulation of thermostat settings and possibly the voltage reduction of lighting and appliances (in some cases including HVAC components) in the building, which may reduce thermal and visual comfort for certain periods. In order to support retrofit and design improvement decisions, an approach is developed that finds the optimal mix of measures that maximize the net present value of the investment in energy flexibility measures over twenty years for the owner. This study will analyze optimal solutions for three commercial building types. Differences between them in terms of energy use and peak demand will be investigated and a generically applicable measure of energy flexibility will be developed. These three buildings are chosen (by scaling their total floor area) such that their demand charges are in the same range. The monetary benefit of energy flexibility will be studied under different demand charge rate structures and under variable building consumption scenarios. This research will result in a new optimization framework for choosing the optimum among multiple options. Based on the proposed framework, this research will determine optimal ways to increase energy flexibility, leading to the best investment decisions for different commercial building types in different locations and under different rate structures.
The use of building integrated photovoltaics (BIPV) towards ultra energy efficient buildings

(Georgia Institute of Technology, 2017-01-03) Kishore, Pranav

This study talks about the energy efficient buildings from demand side of management as well as supply side of management too. It mainly covers feasibility and challenges of BIPV integration in the buildings. This study can also be used for deciding that whether a dynamic simulation modeling is required about the feasibility of BIPV integration in a commercial building.
Integrated performance based design of communities and distributed generation

(Georgia Institute of Technology, 2016-08-26) Street, Michael

The vertically integrated utility market within the U.S. is undergoing rapid changes due to the rise of small-scale distributed power generation known as microgrids, which are local networks of power generation and distribution typically serving a demand less than 40 MW. Primary drivers for microgrid investment are the performance benefits these systems return to their owners, which include increased reliability, reduced emissions and reduced operating costs. We define a novel modeling methodology to represent the microgrid as an integrated system of the demand and supply. Previous work to develop an integrated system model does not adequately model the building thermal demand, incorporate a modeler’s knowledge of the grid’s availability or allow for a user to model their tolerance for unmet demand. To address these modeling issues, we first demonstrate a technique for representing a building stock as a reduced order hourly demand model. Next, as demand side measures are typically defined at the building level as discrete options, we demonstrate a technique for converting a large discrete optimization problem into a simplified continuous variable optimization problem through the use of Pareto efficient cost functions. The reduced problem specification results in 90% fewer function evaluations for a benchmark optimization task. Then, we incorporate two new features into the Distributed Energy Resource Customer Adoption Model (DER-CAM) developed by Lawrence Berkeley National Laboratory (LBNL) that allow users to define grid outage scenarios and their limit of expected energy demand not served. Applying the integrated model to a microgrid design scenario return solutions that exhibit on average an 8% total annual cost reduction and 18% reduction in CO2 emissions versus a Supply Only case. Similarly, the results on average reduce total annual cost by 5% and annual emissions by 17% for a Demand First case. In summary, we present a modeling methodology with application to joint decision making that involve renewable power supply, building systems and passive building design measures and recommend this model for performance based microgrid design.
The impact of occupant modeling on energy outcomes of building energy simulation

(Georgia Institute of Technology, 2016-08-02) Kim, Ji Hyun

The reported performance gap between predicted and real building energy consumption has drawn keen attention from the building simulation community and related stakeholders. Alongside other research efforts to identify, quantify, and close this gap, the most recent attempt is the development of occupant behavior models that generate more “realistic” occupant inputs in the building energy simulation used for prediction. These new occupant models are typically realized by stochastic methods. To date, the newly developed models focus on mimicking real life variability. In spite of that, they have not led to more accurate consumption predictions than previous methods. Rather than adding yet another occupant behavior modeling approach, this thesis emphasizes the need to understand the impact of occupant models on building energy outcomes in real life applications. To accomplish this, we investigate two distinctive approaches to occupant modeling: top-down and bottom-up. We build the argument in the thesis that the top-down approach is suitable in highly variable situations where relatively little information about actual occupant variables can be known. This is usually the case in residential applications. By introducing a so-called “Life Style Factor,” we conclude that the use of this factor is promising to capture the variability of occupant-related parameters in residential buildings. For commercial buildings, a fundamental analysis is conducted to identify the impact of occupant-related inputs on the performance gap while explicitly considering the level of modelers’ knowledge about occupants’ presence and actions at the time of prediction. The results of a sensitivity analysis reveal that even in the case where the modelers’ ignorance of actual occupancy is significant and hence occupant parameters become important contributors to the performance gap, the resulting disparity could be fairly well quantified without introducing complex occupant behavior models. It is also found that the randomness of occupant behavior with respect to actions has no significant role in the performance gap at least in typical building simulation practice. This finding is significant as it advises us to rethink our pursuit of accuracy by developing new occupant behavior models, such as the ones that explicitly model the human reasoning, perception and action related to the opening of windows.
Accuracy, validity and relevance of probabilistic building energy models

(Georgia Institute of Technology, 2016-08-02) Wang, Qinpeng

Residential and commercial buildings consume 41% of total U.S. energy consumption. Since improving energy efficiency is still the most cost efficient energy saving option in the U.S., it is not surprising that many new buildings represent a push towards ultra-efficiency. Many studies argue that this calls for the use of high fidelity prediction models that by necessity will be probabilistic in nature due to many sources of uncertainty that affect the translation of a design specification into the actual reality of a constructed and operated facility. To inspect the fidelity of these probabilistic models against traditional deterministic models, we pose questions that address three major aspects of this new generation of building energy models: • Accuracy: do these models give more “correct” answers? • Validity: do these models lead to “better” design/retrofit decisions? • Relevance: does a profession that deploys these models provide “higher” value to the industry? This dissertation addresses the first question by identifying gaps in our understanding and quantifying various sources of model uncertainty reported in recent literature. Insufficiently understood and not well-quantified sources are further studied and resolved. The results of the above are analyzed in a sensitivity analysis that ranks input parameters alongside with model form uncertainties. Next, we adapt proven methods to conduct verification of probabilistic building energy models. Probabilistic calibration, marginal calibration and a continuous rank probability score are used to evaluate the “correctness” of the new generation of models. We illustrate the challenges of delivering validity proofs in a case study where outcomes of uncertainty analysis are translated into (monetary) risks and their influence is analyzed in a decision-making scenario involving energy performance contracts. Lastly, the study introduces a speculative approach to proving relevance by quantifying the overall societal benefit of a transparent risk framework that has the potential to unlock currently stagnating capital flow into large-scale building retrofits.