C. Lindsay Anderson

Mobility of Multiwalled Carbon Nanotubes in Porous Media

Engineered multiwalled carbon nanotubes (MWCNTs) are the subject of intense research and are expected to gain widespread usage in a broad variety of commercial products. However, concerns have been raised regarding potential environmental and human health risks. The mobility of MWCNTs in porous media is examined in this study using one-dimensional flow-through column experiments under conditions representative of subsurface and drinking water treatment systems. Results demonstrate that pore water velocity strongly influenced MWCNT transport, with high MWCNT mobility at pore water velocities greater than 4.0 m/d. A numerical simulator, which incorporated a newly developed theoretical collector efficiency relationship for MWCNTs in spherical porous media, was developed to model observed column results. The model, which incorporated traditional colloid filtration theory in conjunction with a site-blocking term, yielded good agreement with observed results in quartz sand-packed column experiments. Experiments were also conducted in glass bead-packed columns with the same mean grain size as the quartz sand-packed columns. MWCNTs were more mobile in the glass bead-packed columns.

Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand

This work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of-the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications, for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method: 1) provides a method to select the number of decomposition levels to denoise; 2) uses a new formula to calculate noise thresholds that does not require noise estimation; 3) uses separate noise thresholds for positive and negative wavelet coefficients; 4) applies denoising to the approximation component; and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. In addition, its computation time is more than six times faster.

A Flexible Dispatch Margin for Wind Integration

Integrating wind power into power systems contributes to existing variability in system operations. Current methods to mitigate this variability and uncertainty focus on using conventional generator ramping capability. There is also the option of using wind power itself to mitigate the variability and uncertainty that it introduces into the system. This paper introduces the concept of a flexible dispatch margin as a means for wind to participate in mitigating net variability and net uncertainty. In providing a flexible dispatch margin, wind generators under-schedule in the hour-ahead energy market in order to have additional expected flexibility available for the real-time market. The implementation of the flexible dispatch margin is analyzed in a two-stage optimization model with recourse to the flexible dispatch margin, flexible demand and generator ramping. This modeling framework combines Monte Carlo simulations with AC OPF analysis, using the IEEE 39-bus test system. Results show that use of the flexible dispatch margin decreases the reliance on peaking generators to mitigate net variability and uncertainty, and also decreases the frequency of price spike events, particularly as wind penetration increases from 10% to 30%. The analysis emphasizes the importance of increasing flexible resource capability as power system variability and uncertainty increase.

A stochastic, contingency-based security-constrained optimal power flow for the procurement of energy and distributed reserve

It is widely agreed that optimal procurement of reserves, with explicit consideration of system contingencies, can improve reliability and economic efficiency in power systems. With increasing penetration of uncertain generation resources, this optimal allocation is becoming even more crucial. Herein, a problem formulation is developed to solve the day-ahead energy and reserve market allocation and pricing problem that explicitly considers the redispatch set required by the occurrence of contingencies and the corresponding optimal power flow, static and dynamic security constraints. Costs and benefits, including those arising from eventual demand deviation and contingency-originated redispatch and shedding, are weighted by the contingency probabilities, resulting in a scheme that contracts the optimal amount of resources in a stochastic day-ahead procurement setting. Furthermore, the usual assumption that the day-ahead contracted quantities correspond to some base case dispatch is removed, resulting in an optimal procurement as opposed to an optimal dispatch. Inherent in the formulation are mechanisms for rescheduling and pricing dispatch deviations arising from realized demand fluctuations and contingencies. Because the formulation involves a single, one stage, comprehensive mathematical program, the Lagrange multipliers obtained at the solution are consistent with shadow prices and can be used to clear the day-ahead and spot markets of the different commodities involved. Co-optimization of energy and reserves, including system contingency requirementsComplete AC power flow formulation with static and dynamic security constraintsLagrange multipliers determine various day-ahead and spot market commodity prices.Comparison with traditional method shows improvements in system security and costs.

Analysis of the System Costs of Wind Variability Through Monte Carlo Simulation

Wind power forecast uncertainty raises concerns of the impact of wind power on power system and electricity market operations. The analysis presented in this paper uses an optimal power flow (OPF) model in a Monte Carlo Simulation (MCS) framework to estimate the cost impacts from the uncertainty in windfarm output. Using various regional load levels, and assumptions on the costs for providing balancing energy, the results from the OPF and MCS show that wind power forecast uncertainty for the test system can increase production cost up to 350 times, though for most cases the forecast uncertainty does not introduce significant changes from the base cases. The real and reactive power losses are shown to be higher for scenarios with low wind-high load and high wind-low load as compared to the moderate wind-load cases.

A Decision Framework for Optimal Pairing of Wind and Demand Response Resources

Day-ahead electricity markets do not readily accommodate power from intermittent resources such as wind because of the scheduling difficulties presented by the uncertainty and variability in these resources. Numerous entities have developed methods to improve wind forecasting and thereby reduce the uncertainty in a day-ahead schedule for wind power generation. This paper introduces a decision framework for addressing the inevitable remaining variability resulting from imperfect forecasts. The framework uses a paired resource, such as demand response, gas turbine, or storage, to mitigate the generation scheduling errors due to wind forecast error. The methodology determines the cost-effective percentage, or adjustment factor, of the forecast error to mitigate at each successive market stage, e.g., 1 h and 10 min ahead of dispatch. This framework is applicable to any wind farm in a region with available pairing resources, although the magnitude of adjustment factors will be specific to each region as the factors are related to the statistics of the wind resource and the forecast accuracy at each time period. Historical wind data from New England are used to illustrate and analyze this approach. Results indicate that such resource pairing via the proposed decision framework will significantly reduce the need for an independent system operator to procure additional balancing resources when wind power participates in the markets.

A Comparison of Robust and Probabilistic Reliability for Systems with Renewables and Responsive Demand

The effective integration of significant levels of intermittent renewable resources in power system operations will be enabled by increased participation of demand-side resources. In this work, the use of these demand-side resources for balancing reserves is examined in the context of solutions with varying degrees of robustness. The objective is a simulation-based analysis of the impact of requiring robust versus non-robust solutions, the latter in the form of chance-constrained solutions. We use a stochastic optimal power flow formulation that leverages various classes of reserves, from both generation and responsive demand, to manage considerable uncertainty in renewable generation. Case studies with a 30-bus system illustrate that reserve allocations under the robust formulation, though reliable, may cause undue stress to the system that could render the dispatch implementation infeasible. Conversely, the flexibility introduced in a chance-constrained formulation, even at risk-averse probability levels, produces more realistic allocations of generation and reserves, and can be adjusted to provide full robustness at critical, or highly uncertain, periods in the planning horizon. This flexibility is advantageous to the system and customizable by the operator.

The Influence of Demand Resource Response Time in Balancing Wind and Load

The integration of demand response resources into wholesale electricity markets facilitates the growth in wind power integration. Available demand resources have different capabilities in terms of response time, as demonstrated by the variety of programs ranging from day-ahead markets to dynamic pricing. This paper analyzes the importance of selecting the most appropriate demand response resources, DRR, for balancing wind variability at different time scales. The paper first considers the power spectral density of wind power and electric load for ISOne region, and identifies benefits in using demand response programs to mitigate wind variability. This comparison provides insight into the time scales most valuable for balancing wind variability. Next, the paper presents empirical tests using OPF and Monte Carlo simulations to analyze the use of DRR for mitigating wind uncertainty. Results show that an optimal combination of hour ahead and 10-minute DRR eliminates price spikes at low and moderate wind penetrations while significantly reducing price spike events at 30% wind penetration.

Enabling renewable resource integration: The balance between robustness and flexibility

The steady rise of electricity demand and renewable energy sources is increasing the need for flexibility to enable power systems to adapt to changes in supply and demand. To this end, demand response programs have the potential to increase the flexibility of the system. In this work, a direct-load-control demand response program is used in the scheduling task of a power system with high levels of variable renewable generation. The model considers different classes of reserves provided by both conventional generation and responsive demand. Unit commitment, generator dispatch and reserve allocations are determined with appropriate risk-averse levels to guarantee a reliable and feasible operation of the system across the planning horizon. Risk preferences are reflected in constraint satisfaction via robust and probabilistically-constrained approaches. Case studies with a 57-bus system show that the probabilistic approach allows higher wind share in the power network and incurs lower costs than the robust approach. In addition, results show that controllable loads are an important contributor to system flexibility, though addition of other classes of responsive demand will also bring desirable flexibility.