No description available.

No description available.

The increasing penetration of distributed generation (DG) in power distribution systems presents technical and economic benefits as well as integration challenges to utility engineers. Governments are beginning to acknowledge DG as an economically viable alternative to deferring investment at generation, transmission and distribution levels, meeting demand growth and improving distribution network performance and security. DG technology is rapidly maturing in Ontario due to government economic incentives promoting connection, specifically, the Ontario’s Feed-In-Tariff (FIT) Program. Optimal sizing and siting of DG is well researched, traditionally studying the technical impact on distribution system such as real power loss reduction and voltage profile improvement. Equally common objectives studied are the economics of DG installation which are useful for the developer when deciding when and where to install. Although DG represents a “non-wires” solution to network asset reinforcement, the direct economic benefit to the host utility from promoting DG uptake is not fully understood by utility planners and asset managers. Some DG based asset reinforcement deferral work has been performed in the UK and Italy but is mainly at the transmission level and is not part of an overall strategy that could be applied by a utility. This research presents a comprehensive three stage technique: optimal siting, optimal sizing and financial evaluation of cost savings over a defined planning period to quantify the economic benefit to a Local Distribution Company (LDC) of solar photovoltaic (PV) DG connections on an actual distribution feeder. Optimal sites for PV DG are determined by applying the power loss sensitivity factor method to the test feeder. The objective functions used to determine cost savings consist of loss minimization, asset investment deferral, and peak demand reduction to identify an optimal DG penetration limit. Furthermore, a utility planner can identify an optimal DG penetration limit, encourage uptake at preferred locations that would benefit the LDC, and use the positive impact of DG at existing locations as part of an asset management strategy to prioritize and schedule future asset reinforcement upgrades.

This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals. / text

The increasing penetration of distributed generation (DG) in power distribution systems presents technical and economic benefits as well as integration challenges to utility engineers. Governments are beginning to acknowledge DG as an economically viable alternative to deferring investment at generation, transmission and distribution levels, meeting demand growth and improving distribution network performance and security. DG technology is rapidly maturing in Ontario due to government economic incentives promoting connection, specifically, the Ontario’s Feed-In-Tariff (FIT) Program. Optimal sizing and siting of DG is well researched, traditionally studying the technical impact on distribution system such as real power loss reduction and voltage profile improvement. Equally common objectives studied are the economics of DG installation which are useful for the developer when deciding when and where to install. Although DG represents a “non-wires” solution to network asset reinforcement, the direct economic benefit to the host utility from promoting DG uptake is not fully understood by utility planners and asset managers. Some DG based asset reinforcement deferral work has been performed in the UK and Italy but is mainly at the transmission level and is not part of an overall strategy that could be applied by a utility. This research presents a comprehensive three stage technique: optimal siting, optimal sizing and financial evaluation of cost savings over a defined planning period to quantify the economic benefit to a Local Distribution Company (LDC) of solar photovoltaic (PV) DG connections on an actual distribution feeder. Optimal sites for PV DG are determined by applying the power loss sensitivity factor method to the test feeder. The objective functions used to determine cost savings consist of loss minimization, asset investment deferral, and peak demand reduction to identify an optimal DG penetration limit. Furthermore, a utility planner can identify an optimal DG penetration limit, encourage uptake at preferred locations that would benefit the LDC, and use the positive impact of DG at existing locations as part of an asset management strategy to prioritize and schedule future asset reinforcement upgrades.

abstract: A major problem faced by electric utilities is the need to meet electric loads during certain times of peak demand. One of the widely adopted and promising programs is demand response (DR) where building owners are encouraged, by way of financial incentives, to reduce their electric loads during a few hours of the day when the electric utility is likely to encounter peak loads. In this thesis, we investigate the effect of various DR measures and their resulting indoor occupant comfort implications, on two prototype commercial buildings in the hot and dry climate of Phoenix, AZ. The focus of this study is commercial buildings during peak hours and peak days. Two types of office buildings are modeled using a detailed building energy simulation program (EnergyPlus V6.0.0): medium size office building (53,600 sq. ft.) and large size office building (498,600 sq. ft.). The two prototype buildings selected are those advocated by the Department of Energy and adopted by ASHRAE in the framework of ongoing work on ASHRAE standard 90.1 which reflect 80% of the commercial buildings in the US. After due diligence, the peak time window is selected to be 12:00-18:00 PM (6 hour window). The days when utility companies require demand reduction mostly fall during hot summer days. Therefore, two days, the summer high-peak (15th July) and the mid-peak (29th June) days are selected to perform our investigations. The impact of building thermal mass as well as several other measures such as reducing lighting levels, increasing thermostat set points, adjusting supply air temperature, resetting chilled water temperature are studied using the EnergyPlus building energy simulation program. Subsequently the simulation results are summarized in tabular form so as to provide practical guidance and recommendations of which DR measures are appropriate for different levels of DR reductions and the associated percentage values of people dissatisfied (PPD). This type of tabular recommendations is of direct usefulness to the building owners and operators contemplating DR response. The methodology can be extended to other building types and climates as needed. / Dissertation/Thesis / M.S. Architecture 2012

abstract: Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were developed to estimate peak demand based on air temperatures, a top-down statistical model and bottom-up spatial building energy model. Both approaches yielded similar results, in that peak demand should increase sub-linearly at temperatures above 40°C (104 °F) due to saturation in the coincidence of air conditioning (AC) duty cycles. Spatial projections for peak demand were developed for LAC to 2060 considering potential changes in population, building type, building efficiency, AC penetration, appliance efficiency, and air temperatures due climate change. These projections were spatially allocated to delivery system components (generation, transmission lines, and substations) to consider their vulnerability in terms of thermal de-rated capacity and weather adjusted load factor (load divided by capacity). Peak hour electricity demand was projected to increase in residential and commercial sectors by 0.2–6.5 GW (2–51%) by 2060. All grid components, except those near Santa Monica Beach, were projected to experience 2–20% capacity loss due to air temperatures exceeding 40 °C (104 °F). Based on scenario projections, and substation load factors for Southern California Edison (SCE), SCE will require 848—6,724 MW (4-32%) of additional substation capacity or peak shaving in its LAC service territories by 2060 to meet additional demand associated with population growth projections. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

No description available.

No description available.

This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working. / Doctor of Philosophy / This dissertation titled "An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings" tackles two important challenges in the energy management domain: –electric grid peak demand curtailment and energy savings in commercial buildings. The distinguishing feature of the proposed solution lies in addressing these challenges solely through demand-side management (DSM) strategies, which include HVAC thermostat setpoint interventions and lighting control. We present a methodology for developing highly accurate building energy models that serve as digital twins of actual buildings. These digital replicas can be used to quantify the impact of various interventions and reflect the realistic operation of commercial buildings across varied conditions. This enables building owners to control demand intelligently and transact energy effectively in the electricity market. The development of Internet of Things (IoT) market and advanced technologies such as smart meters and smart thermostats allows for the design of novel strategies that address traditional challenges faced by electric grid operators. This dissertation elaborates on how smart buildings can leverage IoT-based solutions to participate in the electricity market during demand control periods. We also developed an expert opinion-based demand curtailment allocation scheme resulting in grid peak demand reduction. The numerical results obtained reinforce the effectiveness of the proposed solution across varied climatic conditions.