Policy Risk and Private Investment in Ontario’s Wind Power Sector

Even though governments may adopt favourable regulatory policies for renewable power generation, their ability to encourage private sector investment depends also on the presence of regulatory governance institutions that provide credible long-term commitments to potential investors. In the case of Ontario we contend that, despite large market potential and comparatively strong regulatory incentive policies, weak regulatory governance is one factor that has accounted for the challenges in attracting and implementing large scale private investment in power generation at a reasonable cost. We find empirical support for our arguments in a unique survey of 63 wind power firms that assessed private sector opinions about the investment environment for renewable energy in Ontario. Compared to a range of factors, firms rated the stability of regulatory policy among the weakest aspects of Ontario?s business environment. However, policy stability ranked among the most important factors in firms? assessments of the attractiveness of alternative jurisdictions in their location decisions. Subsequent interviews revealed that firms have responded to this risk in Ontario by explicitly pricing it into wind project financial models – implying higher wind power prices for ratepayers – and by directing investment funds to other jurisdictions. We argue that policy stability in Ontario may be improved by devolving greater decision-making authority to regulatory agencies in the energy sector and by strengthening their institutional independence.

AMPTE/CCE‐SCATHA simultaneous observations of substorm‐associated magnetic fluctuations

This study examines substorm-associated magnetic field fluctuations observed by the AMPTE/CCE and SCATHA satellites in the near-Earth tail. Three tail reconfiguration events are selected, one event on August 28, 1986, and two consecutive events on August 30, 1986. The fractal analysis was applied to magnetic field measurements of each satellite. The result indicates that (1) the amplitude of the fluctuation of the north-south magnetic component is larger, though not overwhelmingly, than the amplitudes of the other two components and (2) the magnetic fluctuations do have a characteristic timescale, which is several times the proton gyroperiod. In the examined events the satellite separation was less than 10 times the proton gyroradius. Nevertheless, the comparison between the AMPTE/CCE and SCATHA observations indicates that (3) there was a noticeable time delay between the onsets of the magnetic fluctuations at the two satellite positions, which is too long to ascribe to the propagation of a fast magnetosonic wave, and (4) the coherence of the magnetic fluctuations was low in the August 28, 1986, event and the fluctuations had different characteristic timescales in the first event of August 30, 1986, whereas some similarities can be found for the second event of August 30, 1986. Result 1 indicates that perturbation electric currents associated with the magnetic fluctuations tend to flow parallel to the tail current sheet and are presumably related to the reduction of the tail current intensity. Results 2 and 3 suggest that the excitation of the magnetic fluctuations and therefore the trigger of the tail current disruption is a kinetic process in which ions play an important role. It is inferred from results 3 and 4 that the characteristic spatial scale of the associated instability is of the order of the proton gyroradius or even shorter, and therefore the tail current disruption is described as a system of chaotic filamentary electric currents. However, result 4 suggests that the nature of the tail current disruption can vary from event to event

Construction of Reduced Order Models for Fluid Flows Using Deep Feedforward Neural Networks

We present a numerical methodology for construction of reduced order models, ROMs, of fluid flows through the combination of flow modal decomposition and regression analysis. Spectral proper orthogonal decomposition, SPOD, is applied to reduce the dimensionality of the model and, at the same time, filter the POD temporal modes. The regression step is performed by a deep feedforward neural network, DNN, and the current framework is implemented in a context similar to the sparse identification of non-linear dynamics algorithm, SINDy. A discussion on the optimization of the DNN hyperparameters is provided for obtaining the best ROMs and an assessment of these models is presented for a canonical nonlinear oscillator and the compressible flow past a cylinder. Then, the method is tested on the reconstruction of a turbulent flow computed by a large eddy simulation of a plunging airfoil under dynamic stall. The reduced order model is able to capture the dynamics of the leading edge stall vortex and the subsequent trailing edge vortex. For the cases analyzed, the numerical framework allows the prediction of the flowfield beyond the training window using larger time increments than those employed by the full order model. We also demonstrate the robustness of the current ROMs constructed via deep feedforward neural networks through a comparison with sparse regression. The DNN approach is able to learn transient features of the flow and presents more accurate and stable long-term predictions compared to sparse regression

Hierarchical QoS routing in next generation optical networks

In this paper, we study the problem of inter-domain routing with two additive QoS constraints in hierarchical optical networks. We develop an inter-domain routing protocol that (1) identifies the QoS supported by the paths, (2) selects an inter-domain path that satisfies the QoS requirement of a connection request, and (3) reserves the wavelength on each link along the path in such a way that the number of wavelength converters needed is minimized. Both formal analyses and extensive simulation experiments show that our inter-domain routing protocol outperforms the existing protocols. © 2006 IEEE.published_or_final_versio

Quality-of-service routing with two concave constraints

Routing is a process of finding a network path from a source node to a destination node. A good routing protocol should find the "best path" from a source to a destination. When there are independent constraints to be considered, the "best path" is not well-defined. In our previous work, we developed a line segment representation for Quality-of-Service routing with bandwidth and delay requirements. In this paper, we propose how to adopt the line segment when a request has two concave constraints. We have developed a series of operations for constructing routing tables under the distance-vector protocol. We evaluate the performance through extensive simulations. ©2008 IEEE.published_or_final_versio

Hop-by-hop routing in wireless mesh networks with bandwidth guarantees

Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths. © 2012 IEEE.published_or_final_versio