• Energy Research

Abstract Climate change is projected to cause an increase in the severity and frequency of extreme weather events such as heat waves and droughts. Such changes present planning and operating challenges and risks to many economic sectors. In the electricity sector, statistics of extreme events in the past have been used to help plan for future peak loads, determine associated infrastructure requirements, and evaluate operational risks, but industry-standard planning tools have yet to be coupled with or informed by temperature models to explore the impacts of the “new normal” on planning studies. For example, high ambient temperatures during heat waves reduce the output capacity and efficiency of gas-fired combustion turbines just when they are needed most to meet peak demands. This paper describes the development and application of a production cost and unit commitment model coupled to high resolution, hourly temperature data and a temperature-dependent load model. The coupled system has the ability to represent the impacts of hourly temperature on load conditions and available capacity and efficiency of combustion turbines, and therefore capture the potential impacts on system reserve and production cost. Ongoing work expands this capability to address the impacts of water availability and temperature on power grid operation.

The emergence of distributed generation has made a case for a deregulated, competitive, transactive energy market, operating at the level of the traditional residential consumer. These new energy players, prosumers, will interact as the larger energy generators do under the supervision of Independent System Operators (ISOs), but with their own Distributed System Operators (DSOs). This work proposes a prosumer energy management scheme, broken into a day-ahead schedule and a realtime adjustment, mirroring the ISO market structure. Within this framework, a dynamic rate can be designed and tested for the prosumer. A time-of-use (TOU) rate was combined with a frequency based, real-time dynamic rate to produce a hybrid rate that the prosumer can optimize for during its day-ahead and real-time dispatch. This hybrid rate can be calculated every one minute and applied autonomously from the grid frequency, providing secondary frequency regulation and an incentive for better solar management and use of energy storage. Such a real-time rate is the first designed for price-reactive control. In simulation, the real-time hybrid rate is compared to conventional TOU and flat rates and the final daily energy costs are calculated for a variety of residential load types with a realistic distributed solar generation curve gathered from Pecan Street Inc. Dataport. Under the one minute hybrid rate, the results indicate a near zero energy bill can be achieved for a prosumer with day-time load and smart use of energy storage.

In this research, a Simulink model of a standalone vehicular solid-oxide fuel cell (SOFC) auxiliary power unit (APU) is developed. The SOFC APU model consists of three major components: a controller model; a power electronics system model; and an SOFC plant model, including an SOFC stack module, two heat exchanger modules, and a combustor module. This paper discusses the development of the nonlinear dynamic models for the SOFC stacks, the heat exchangers and the combustors. When coupling with a controller model and a power electronic circuit model, the developed SOFC plant model is able to model the thermal dynamics and the electrochemical dynamics inside the SOFC APU components, as well as the transient responses to the electric loading changes. It has been shown that having such a model for the SOFC APU will help design engineers to adjust design parameters to optimize the performance. The modeling results of the SOFC APU heat-up stage and the output voltage response to a sudden load change are presented in this paper. The fuel flow regulation based on fuel utilization is also briefly discussed.

This paper presents a real-time greedy-index dispatching policy (GIDP) for using plug-in electric vehicles (PEVs) to provide frequency regulation services. A new service cost allocation mechanism is proposed to award PEVs based on the amount of service they provided, while considering compensations for delayed-charging and reduction of battery lifetime due to participation of the service. The GIDP transforms the optimal dispatch problem from a high-dimensional space into an 1-D space while preserving the solution optimality. When solving the transformed problem in real-time, the global optimality of the GIDP solution can be guaranteed by mathematically proved “indexability.” Because the GIDP index can be calculated upon the PEV’s arrival and used for the entire decision making process till its departure, the computational burden is minimized and the complexity of the aggregator dispatch process is significantly reduced. Simulation results are used to evaluate the proposed GIDP, and to demonstrate the potential profitability from providing frequency regulation service by using PEVs.

This report presents a multi-disciplinary modeling approach to quickly quantify climate change impacts on energy consumption, peak load, and load composition of residential and commercial buildings. This research focuses on addressing the impact of temperature changes on the building cooling load in 10 major cities across the Western United States and Canada. Our results have shown that by the mid-century, building yearly energy consumption and peak load will increase in the Southwest. Moreover, the peak load months will spread out to not only the summer months but also spring and autumn months. The Pacific Northwest will experience more hot days in the summer months. The penetration of the air conditioning (a/c) system in this area is likely to increase significantly over the years. As a result, some locations in the Pacific Northwest may be shifted from winter peaking to summer peaking. Overall, the Western U.S. grid may see more simultaneous peaks across the North and South in summer months. Increased cooling load will result in a significant increase in the motor load, which consumes more reactive power and requires stronger voltage support from the grid. This study suggests an increasing need for the industry to implement new technology to increase the efficiency of temperature-sensitive loads and apply proper protection and control to prevent possible adverse impacts of a/c motor loads.

This paper proposes a new approach to estimate dominant mode for monitoring inter-area oscillation in the China Southern power grid (CSG) by the use of phasor measurement units (PMUs) under both ringdown and ambient conditions. The state space model is identified by the data driven stochastic subspace identification (Data-SSI) algorithm. The canonical variate algorithm is used first to construct the weighted projection matrix of the Data-SSI. Then, the criterion for model order selection is developed to estimate the model order, and the linear model of power system is built with Data-SSI. The dominant oscillation modes are calculated by eigenvalue analysis. To accurately identify the dominant modes, repetitive results are calculated with model order variation, and then clustering analysis and stepwise refinement are applied to discriminating the dominant modes from trivial ones to improve the estimation accuracy. Field-measurement data collected by PMUs in CSG is used to validate the proposed algorithm. The comparison between existing mode estimation techniques and the proposed approach demonstrates its accuracy and robustness under both ringdown and ambient conditions.

The need for affordable, reliable, sustainable, and modern energy is now more important than ever because of the climate crisis. Climate change will push up to 130 million people into poverty over the next 10 years and continue to cause more unpredictable natural disasters, such as cyclones, flooding, earthquakes, landslides, tsunamis, and volcanic eruptions. Power outages do not occur only in remote rural areas but also in developed countries, lasting for several hours and even a couple of days, due to the extreme weather in recent years. Microgrids, as a flexible architecture capable of integrating local distributed energy resources (DERs), can satisfy wide-ranging demands via their variable solutions, from off-grid to on-grid applications.

This paper presents a novel dynamic parameter selection process to optimize the performance of a centralized load controller designed to provide intra-hour load balancing services using thermostatically controlled appliances (TCAs). An optimal set of control parameters for the controller are selected by exhaustive simulations of control variables such as the sampling time of the forecaster, the magnitude of the load balancing signal, and the temperature deadband. The effects of TCA lock-off times, ambient temperatures, heat gains, and two-way communication delays on the controller design are also modeled. Customer comfort, device life cycles, and control errors are used as metrics to evaluate the performance. The results demonstrate that the optimized controller offers satisfactory performance considering all the operational uncertainties.