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Open Access Publications from the University of California

Scholarly Works (35 results)

  • Article
  • Peer Reviewed
LBL Publications (2010)
    Cover page: Study on Auto-DR and Pre-Cooling of Commercial Buildings with Thermal Mass in California
    • Article
    • Peer Reviewed
    LBL Publications (2008)

    Over the several past years, Lawrence Berkeley National Laboratory (LBNL) has conducted field tests for different pre-cooling strategies in different commercial buildings within California. The test results indicated that pre-cooling strategies were effective in reducing electric demand in these buildings during peak periods. This project studied how to optimize pre-cooling strategies for eleven buildings in the Tri-City Corporate Center, San Bernardino, California with the assistance of a building energy simulation tool -- the Demand Response Quick Assessment Tool (DRQAT) developed by LBNL's Demand Response Research Center funded by the California Energy Commission's Public Interest Energy Research (PIER) Program. From the simulation results of these eleven buildings, optimal pre-cooling and temperature reset strategies were developed. The study shows that after refining and calibrating initial models with measured data, the accuracy of the models can be greatly improved and the models can be used to predict load reductions for automated demand response (Auto-DR) events. This study summarizes the optimization experience of the procedure to develop and calibrate building models in DRQAT. In order to confirm the actual effect of demand response strategies, the simulation results were compared to the field test data. The results indicated that the optimal demand response strategies worked well for all buildings in the Tri-City Corporate Center. This study also compares DRQAT with other building energy simulation tools (eQUEST and BEST). The comparison indicate that eQUEST and BEST underestimate the actual demand shed of the pre-cooling strategies due to a flaw in DOE2's simulation engine for treating wall thermal mass. DRQAT is a more accurate tool in predicting thermal mass effects of DR events.

      Cover page: Auto-DR and Pre-cooling of Buildings at Tri-City Corporate Center
      • Article
      LBL Publications (2010)

      This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30percent using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

        Cover page: Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California
        • Article
        LBL Publications (2015)
        In 2006, the Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory (LBNL) initiated the development of a quick assessment tool for demand response in buildings and, in 2007 the DRRC released the first version of the Demand Response Quick Assessment Tool (DRQAT) for public use. Over the past few years, the DRRC has been improving the DRQAT tool based on users’ feedback and upgrading the engine with the EnergyPlus energy simulation tool. Currently, DRQAT enables users to evaluate a single DR strategy configuration at a time. Users could greatly benefit from being able to run multiple strategy configurations at a time and directly compare their performance in a single output report. The latest update of DRQAT, described in this report, enables users to do just that to compare different pre-cooling and reset strategies. Also, to help customers better understand the demand response performance of their facilities; this report presents several case studies to compare demand response predictions with measured values. A previous study indicated that the predictive value of the DRQAT simulation model could be significantly improved after calibrating the model with measured data. Most users are not familiar with model calibration, a process that can be time consuming. This report shows a comparison of DRQAT results generated as a typical user would—without calibration. The results show that the DRQAT tool can generate credible predictions of peak demand savings and load shapes throughout demand response event hours.
          Cover page: IMPROVEMENT OF DEMAND RESPONSE QUICK ASSESSMENT TOOL (DRQAT) AND TOOL VALIDATION CASE STUDIES:
          • Article
          LBL Publications (2014)
          The use of simulation to evaluate energy-efficient operations, commissioning problems, and demand-response (DR) strategies offers important insights into building operations. This paper describes a step-by-step procedure for using measured end-use energy data from a campus building to calibrate a simulation model developed in EnergyPlus. This process included identification of key input parameters for reducing uncertainties in the model. The building geometry and internal thermal zones were modeled to match the actual heating ventilation and air conditioning (HVAC) zoning for each individual variable air-volume (VAV) zone. We evaluated most key building and HVAC system components, including space loads (actual occupancy number, lighting and plug loads), HVAC air-side components (VAV terminals, supply and return fans) and water-side components (chillers, pumps, and cooling towers). Comparison of the pre- and post-calibration model shows that the calibration process greatly improves the model's accuracy for each end use. We propose an automated model calibration procedure that links the model to a real-time data monitoring system, allowing the model to be updated any time. The approach enables the automated data feed from sMAP into the EnergyPlus model to create realistic schedules of space loads (occupancy, lighting and plug), performance curves of fans, chillers and cooling towers. We also field-tested DR control strategies to evaluate the model's performance in predicting dynamic response effects. Finally, this paper describes application of the calibrated model to analyze control systems and DR strategies with the goal of reducing peak demand. We compare end-use data from modeled and actual DR events.
            Cover page: Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation
            • Article
            • Peer Reviewed
            LBL Publications (2019)
            This report describes research to characterize the status of, and trends in, big-data analytics for the electricity grid. The research focused on 1) identifying power-grid big-data sources, types, and characteristics; and 2) characterizing big-data architecture, analytic methods, technology applications, and challenges. The first part of this report describes the main sources and characteristics of big data for the smart grid and comprehensively reviews big-data architecture, technologies, and applications in the power sector. The second part of this report presents case studies of big data applications in the power industry: (1) a smart-meter data and predictive analytics method for demand response (DR), (2) synchrophasor data analytics for the distribution grid, and (3) utility data for peak-demand management. For the predictive analytics case study, smart-meter-data-driven and physical models were developed to predict the potential kilowatt (kW) capacity reduction from DR. The DR estimation framework that was developed works for both small and large-scale customers. The synchrophasor case study demonstrates use of an algorithm applied to time-series data to detect events that appear as significant changes, known as “edges,” in voltage magnitude measurements. The synchrophasor case study also introduces an approach for clustering sets of events to reveal unique features that distinguish them (e.g., distinguishing capacitor bank switching from transformer tap changes). The peak-demand management case study describes the use of the data analytics to enable DR programs to limit forecasted peak demand, resulting in cost savings to the utility. The findings from the research described in this report support identification of opportunities and technologies for big-data and analytics applications for demand-side management in the power sector as well as other approaches to modernizing the electricity grid.
              Cover page: Big-Data Analytics for Electric Grid and Demand-Side Management
              • Article
              • Peer Reviewed
              LBL Publications (2024)
              Abstract. The buildings sector in Indonesia is dominated by homes—the number of which has been growing rapidly to meet population-driven demand. However, most buildings built over the past four to five decades lack adequate natural ventilation and other passive cooling measures suited to tropical climates and rely at least partly on air-conditioning. This study focuses on a parametric analysis of passive and low-energy “whole-building” cooling solutions for the tropical climate of Indonesia, evaluating the benefits of 12 passive or low-energy cooling solutions: solar-reflective “cool” roofs, cool walls, insulated roofs, insulated walls, low thermal- emissivity (“low-E”) windows, solar-control window films, ceiling fans with temperature setback, ceiling fans without temperature setback, exterior window shades, interior window shades, natural ventilation based on schedule and outside air temperature, and natural ventilation based only on outside air temperature. Results indicate that a ceiling fan with thermostat setback (raising the cooling setpoint to 28.1 °C from 25 °C while maintaining constant thermal sensation) provided both the highest nationwide electricity savings and the highest net present value for households, followed by insulated walls, insulated roofs, and cool walls.
                Cover page: Evaluation of various passive cooling solutions in decarbonizing the Indonesian residential building sector in tropical climatesCreative Commons 'BY' version 4.0 license
                • Article
                LBL Publications (2015)
                  Cover page: Characterization and Effectiveness of Technologies for India's Electric Grid Reliability and Energy Security
                  • Article
                  LBL Publications (2017)
                  Automated demand response (Auto-DR) is expected to close the loop between buildings and the grid by providing machine-to-machine communications to curtail loads without the need for human intervention. Hence, it can offer more reliable and repeatable demand response results to the grid than the manual approach and make demand response participation a hassle-free experience for customers. However, many building operators misunderstand Auto-DR and are afraid of losing control over their building operation. To ease the transition from manual to Auto-DR, we designed and implemented granular control of Auto-DR systems so that building operators could modify or opt out of individual load-shed strategies whenever they wanted. This paper reports the research findings from this effort demonstrated through a field study in large commercial buildings located in New York City. We focused on (1) understanding how providing granular control affects building operators’ perspective on Auto-DR, and (2) evaluating the usefulness of granular control by examining their interaction with the Auto-DR user interface during test events. Through trend log analysis, interviews, and surveys, we found that: (1) the opt-out capability during Auto-DR events can remove the feeling of being forced into load curtailments and increase their willingness to adopt Auto-DR; (2) being able to modify individual load-shed strategies allows flexible Auto-DR participation that meets the building’s changing operational requirements; (3) a clear display of automation strategies helps building operators easily identify how Auto-DR is functioning and can build trust in Auto-DR systems.
                    Cover page: Effects of Granular Control on Customers’ Perspective and Behavior with Automated Demand Response Systems:
                    • Article
                    • Peer Reviewed
                    LBL Publications (2020)
                    The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. Various objectives have been investigated before, such as following a given load profile, reducing load fluctuation and minimizing the total operational cost. However, the uncertainties inside the behaviors of individual drivers have tremendous impact on the cost effectiveness of microgrid operations, which have not been carefully studied. In this paper, we propose a predictive management strategy in a community microgrid, using datasets of system base load, solar generation and EV charging behaviors from real-world cases. Two stage operations are modeled for cost-effective EV management, i.e. wholesale market partition in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.
                      Cover page: Predictive Management of Electric Vehicles in a Community Microgrid
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