Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item OPTIMAL SCHEDULING OF RESIDENTIAL DEMAND RESPONSE USING DYNAMIC PROGRAMMING(2019) Moglen, Rachel Lee; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electricity price volatility in the Electricity Reliability Council of Texas (ERCOT) poses significant financial threat to many players in the electricity market, though most of the financial burden falls on the retail electric providers (REPs). REPs are contractually obligated to purchase and provide all electricity that the end-user wishes to consume, even when the purchase of this electricity brings them financial losses. Electricity prices in ERCOT can increase from typical ranges ($30/MWh) to $3,000/MWh in as little as 15 minutes, causing REPs to seek mitigation techniques to avoid paying price spike prices for electricity. We explore two techniques for REPs to schedule mitigation techniques: a price prediction-based heuristic approach, as well as an optimal scheduling algorithm using dynamic programming (DP). We aim to optimally schedule these mitigation techniques which shift load from high price periods to times of lower prices, called demand response (DR). To achieve this load shifting, REPs remotely manipulate internet-connected thermostats of residential customers, thereby controlling a fraction of residential HVAC load. We found that the price prediction approach was highly unreliable, even for predicting prices as near as 5 minutes out. We therefore chose to rely on the DP as the primary scheduling model. By applying the DP deterministically to historical electricity price and weather data, the load-shifting technique is shown to potentially improve REP profit margins by 10% to 25% per customer annually. Most of these savings come from a few crucial events, highlighting the usefulness of the DP and the importance of accuracy in the timing of DR events. Due to the uncertainty in electricity prices, we apply a multi-objective approach considering the REP’s conflicting objectives: maximizing savings and minimizing financial risk. Results from this multi-objective formulation point to shorter duration DR events in the evening being the least risky, with additional savings possible through riskier short midday events. To ensure that REPs could apply our DP formulation for use in near real-time decision-making applications, the computation speed was verified to be under one second for 24 stages (i.e., 1-hour intervals for one day.)Item CO2 TRANSCRITICAL REFRIGERATION WITH MECHANICAL SUBCOOLING: ENERGY EFFICIENCY, DEMAND RESPONSE AND THERMAL STORAGE(2018) Bush, John; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines two important concepts: improvements to transcritical carbon dioxide (CO2) refrigeration systems being deployed in supermarkets, and their potential use for demand response and load shifting in a utility-connected application. As regulatory pressure increases to reduce the use of ozone depleting and greenhouse gases as refrigerants, the heating, ventilation, air conditioning and refrigeration (HVAC&R) industry is moving towards alternative refrigerants including natural substances such as carbon dioxide. CO2 has already gained traction as the refrigerant of choice for supermarket applications in some countries, but deployment in warmer climates has been slower due to concerns over efficiency when the cycle operates in transcritical mode. Among the cycle enhancements considered to overcome these concerns is the use of dedicated mechanical subcooling. Laboratory testing was performed on a transcritical booster system with mechanical subcooling to quantify the system performance with and without the subcooler. Data was used to develop and validate transient models, which in turn were used to study the system-wide effects of demand response, particularly short-term shedding of medium or low temperature load. Systems can provide value to the electric grid if they can be responsive to changes in electric utility generation, as indicated by direct calls to shed load or price signals. To further expand the potential usefulness of the refrigeration cycle in grid-interactive operation, the integration of thermal storage is considered. In particular, the integration of thermal storage into the subcooling system is investigated. The mechanical subcooler is used to “charge” a storage media (such as water or another phase change material) overnight, and the storage media allows the subcooler to turn off during peak hours. This allows the system to shift load and allow temporary reduction in electric power usage without a reduction in delivered refrigerating capacity. These two paths are potentially complementary: the load shifting of the integrated thermal storage provides long-term load reduction, while direct load shedding in evaporators allows more agile, short-term reductions. The models developed and validated with laboratory data and expanded upon with thermal energy storage and demand response approaches provide new learnings into enhanced load shifting and demand response capability. The findings of this work show that particularly in time-of-use rate structures with a high ratio of on-peak to off-peak pricing, the thermal storage and load shedding strategies here can provide a reduction in total refrigerating energy cost, even though the changes proposed introduce a slight increase in daily energy under the simulated conditions. In a simulated hot day for Baltimore, Maryland, the energy consumption was 2.6% higher using the thermal storage system than without. In the most extreme case, comparing an aggressive real-world Time-of-Use rate with thermal storage and load shedding against a flat-rate case from the same utility and no controls or storage, a cost savings reduction of 21% was calculated. Comparing baseline operation against a controlled load-shifting strategy under the same time-of-use rate plan, the cost reduction was in the range of 2.8-8.7% depending upon the specific plan.Item PERFORMANCE AND APPLICATIONS OF RESIDENTIAL BUILDING ENERGY GREY-BOX MODELS(2013) Siemann, Michael James; Kim, Jungho; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The electricity market is in need of a method to accurately predict how much peak load is removable by directly controlling residential thermostats. Utilities have been experimenting with residential demand response programs for the last decade, but inconsistent forecasting is preventing them from becoming a dependent electricity grid management tool. This dissertation documents the use of building energy models to forecast both general residential energy consumption and removable air conditioning loads. In the models, complex buildings are represented as simple grey-box systems where the sensible energy of the entire indoor environment is balanced with the flow of energy through the envelope. When internet-connected thermostat and local weather data are inputs, twelve coefficients representing building parameters are used to non-dimensionalize the heat transfer equations governing this system. The model's performance was tested using 559 thermostats from 83 zip codes nationwide during both heating and cooling seasons. For this set, the average RMS error between the modeled and measured indoor air temperature was 0.44°C and the average daily ON time prediction was 1.9% higher than the data. When combined with smart power meter data from 250 homes in Houston, TX in the summer of 2012 these models outperformed the best traditional methods by 3.4 and 28.2% predicting daily and hourly energy consumption with RMS errors of 86 and 163 MWh. The second model that was developed used only smart meter and local weather data to predict loads. It operated by correlating an effective heat transfer metric to past energy data, and even further improvement forecasting loads were observed. During a demand response trial with Earth Networks and CenterPoint Energy in the summer of 2012, 206 internet-connected thermostats were controlled to reduce peak loads by an average of 1.13 kW. The thermostat building energy models averaged forecasting the load in the 2 hours before, during, and after these demand response tests to within 5.9%. These building energy models were also applied to generate thermostat setpoint schedules that improved the energy efficiency of homes, disaggregate loads for home efficiency scorecards and remote energy audits, and as simulation tools to test schedule changes and hardware upgrades.