A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Classifying Dominant Congested Path Using Correlation Factors

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples

Definition of a Balancing Point for Electricity Transmission Contracts

Electricity transmission contracts allocate scarce resources, allow hedging against locational price differences and provide information to guide investment. Liquidity is increased if all transmission contracts are defined relative to one balancing point, then a set of two contracts can replicate any point to point contract. We propose an algorithm and apply it to the European electricity network to identify a well connected balancing point that exhibits minimal relative cross-price responses and hence reduces market power exercised by generation companies. Market level data which is difficult to obtain or model such as price levels in different regions or that is dependent on the time scale of interaction, as demand elasticity, is not required. The only critical input quantities are assumptions on future transmission constraint patterns

Spatial Dispersion of Peering Clusters in the European Internet

We study the role played by geographical distance in the peering decisions between Internet Service Providers. Firstly, we assess whether or not the Internet industry shows clustering in peering; we then concentrate on the dynamics of the agglomeration process by studying the effects of bilateral distance in changing the morphology of existing peering patterns. Our results show a dominance of random spatial patterns in peering agreements. The sign of the effect of distance on the peering decision, driving the agglomeration/dispersion process, depends, however, on the initial level of clustering. We show that clustered patterns will disperse in the long run

‘Definition of a Balancing Point for Electricity Transmission Contracts’

Electricity transmission contracts allocate scarce resources, allow hedging against locational price differences and provide information to guide investment. Liquidity is increased if all transmission contracts are defined relative to one balancing point, then a set of two contracts can replicate any point to point contract. We propose an algorithm and apply it to the European electricity network to identify a well connected balancing point that exhibits minimal relative cross-price responses and hence reduces market power exercised by generation companies. Market level data which is difficult to obtain or model such as price levels in different regions or that is dependent on the time scale of interaction, as demand elasticity, is not required. The only critical input quantities are assumptions on future transmission constraint patterns.Transmission contract design, Congestion management, Market Power, European electricity network

Predicting travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways. The paper uses two different concepts of travel time variability. They differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of the day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to re ect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor of variability. On average, longer delays are associated with higher variability. However, the derivative of travel time variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed trafic 'regimes' (free- ow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can be used as an approximation

How Effective are Toll Roads in Improving Operational Performance?

The main focus of this research is to develop a systematic analytical framework and evaluate the effect of a toll road on region’s traffic using travel time and travel time reliability measures. The travel time data for the Triangle Expressway in Raleigh, North Carolina, United States was employed for the assessment process. The spatial and temporal variations in the travel time distributions on the toll road, parallel alternate route, and near-vicinity cross-streets were analyzed using various travel time reliability measures. The results indicate that the Triangle Expressway showed a positive trend in reliability over the years of its operation. The parallel route reliability decreased significantly during the analysis period, whereas the travel time reliability of cross-streets showed a consistent trend. The stabilization of travel time distributions and the reliability measures over different years of toll road operation are good indicators, suggesting that further reduction in performance measures may not be seen on the near vicinity corridors. The findings from link-level and corridor-level analysis may help with transportation system management, assessing the influence of travel demand patterns, and evaluating the effect of planned implementation of similar projects