Browsing by Subject "Demand response"
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Item Demand side load control in residential buildings with HVAC controller for demand response(2015-05) Yoon, Ji Hoon; Baldick, Ross; Novoselac, Atila; Arapostathis, Aristotle; Liedl, Petra G; Kwasinski, AlexisDemand Response (DR) is a key factor to increase the efficiency of the power grid and has the potential to facilitate supply-demand balance. Demand side load control can contribute to reduce electricity consumption through DR programs. Especially, Heating, Ventilating and Air Conditioning (HVAC) load is one of the major contributors to peak loads. In the United States, HVAC systems are the largest consumers of electrical energy and a major contributor to peak demand. In this research, the Dynamic Demand Response Controller (DDRC) is proposed to reduce peak load as well as saves electricity cost while maintaining reasonable thermal comfort by controlling HVAC system. To reduce both peak load and energy cost, DDRC controls the set-point temperature in a thermostat depending on real-time price of electricity. Residential buildings are modeled with various internal loads using building energy modeling tools. The weather data in different climate zones are used to demonstrate that DDRC decreases peak loads and brings economic benefit in various locations. In addition, two different types of electricity wholesale markets are used to generate DR signals. To assess the performance of DDRC, the control algorithms are improved to consider the characteristics of building envelopes and HVAC equipment. Also, DDRC is designed to be deployed in various areas with different electricity wholesale markets. The indoor thermal comfort on temperature and humidity are considered based on ASHRAE standard 55. Finally, DDRC is developed to a hardware using embedded system. The hardware of DDRC is based on Advanced RISC Microcontroller (ARM) processor and senses both indoor and outdoor environment with Internet connection capability for DR. In addition, user friendly Graphic User Interface (GUI) is generated to control DDRC.Item Economic forecasting and optimization in a smart grid built environment(2013-08) Sriprasad, Akshay; Edgar, Thomas F.This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom Edgar, in the area of modeling and systems optimization for the smart grid. The scope this report includes the development and validation of strategies to elicit demand response, defined as reduction of peak demand, at the residential level, in conjunction with collaborative research efforts from the Pecan Street Research Institute, a smart grid research consortium based in Austin, TX. The first project outlined is an artificial neural network-‐based demand forecasting model, initially developed for UT’s campus cooling system and adapted for residential homes. Utilizing this forecasting model, a number of demand response-‐focused optimization studies are carried out, including optimization of community energy storage for peak shifting, and electric vehicle charging optimization to harness inexpensive night-‐time Texas wind energy. Community energy storage and electric vehicles are chosen as ideal dynamic charging media due to increased proliferation and focus of Pecan Street Research Institute on critical emerging technologies. As these two technologies involve significant capital investment, an alternative mobile application-‐based demand response strategy is outlined to complete a comprehensive portfolio of demand response strategies to suit a variety of budgets and capabilities.Item Sustainable energy roadmap for Austin : how Austin Energy can optimize its energy efficiency(2010-12) Johnston, Andrew Hayden, 1979-; Oden, Michael; Spelman, WilliamThis report asks how Austin Energy can optimally operate residential energy efficiency and demand side management programs including demand response measures. Efficient energy use is the act of using less energy to provide the same level of service. Demand side management encompasses utility initiatives that modify the level and pattern of electrical use by customers, without adjusting consumer behavior. Demand side management is required when a utility must respond to increasing energy needs, or demand, by its customers. In order to achieve the 20% carbon emissions and 800 MW peak demand reductions mandate of the Generation, Resource and Climate Plan, AE must aggressively pursue an increase in customer participation by expanding education and technical services, enlist the full functionality of a smart grid and subsequently reduce energy consumption, peak demand, and greenhouse gas emissions. Energy efficiency is in fact the cheapest source of energy that Austin Energy has at its disposal between 2010 and 2020. But this service threatens Austin Energy’s revenues. With the ascent of onsite renewable energy generation and advanced demand side management, utilities must address the ways they generate revenues. As greenhouse gas emissions regulations lurk on the horizon, the century-old business model of “spinning meters” will be fundamentally challenged nationally in the coming years. Austin Energy can develop robust analytical methods to determine its most cost-effective energy efficiency options, while creating a clear policy direction of promoting energy efficiency while addressing the three-fold challenges of peak demand, greenhouse gas emissions and total energy savings. This report concludes by providing market-transforming recommendations for Austin Energy.Item Utility management of plug-in electric vehicle residential charging(2014-05) Hernandez, Guillermo, active 21st century; Baldick, Ross; Webber, Michael E., 1971-The purpose of this study is to identify realistic opportunities and barriers regarding PEV charge management by analyzing real-world PEV data from customers in the Austin Energy service area and evaluating direct, quantifiable economic value benefits as it relates new revenue, cost avoidance, CO2 reductions, and MW potential for peak shaving. The main objective is to provide business analysis to support the strategic road-map for Austin Energy PEV home charging programs. Three main charge program implementations are considered: Uncontrolled Charging, Time of Use Rates, and One Way Utility Control. The data used for the analysis includes 45 households with PEVs from Mueller area; 24 were under a Time of Use trial with pricing incentives to charge at night, and 21 receive normal Austin Energy rates. Data analysis shows that 66% of Time of Use trial group successfully shifted PEV load to Off Peak hours (10:00PM to 6:00AM). The potential of One Way control, based on load availability for interruption, shows that it will not be possible to implement until there are 37,000 PEVs in the Austin Energy area. Uncontrolled Charging represents a risk by increasing load during the residential peak. Time of Use Rates program will incentivize load shifting, reduce wholesale energy costs for Austin Energy while allowing customers to reduce their overall electricity bill.