Congestion Management

Existing congestion control mechanisms in interconnects can be divided into two general approaches. One is to throttle traffic injection at the sources that contribute to congestion, and the other is to isolate the congested traffic in specially designated resources. These two approaches have different, but non-overlapping weaknesses. In this paper we present in detail a method that combines injection throttling and congested-flow isolation. Through simulation studies we first demonstrate the respective flaws of the injection throttling and of flow isolation. Thereafter we show that our combined method extracts the best of both approaches in the sense that it gives fast reaction to congestion, it is scalable and it has good fairness properties with respect to the congested flows.

In our ecosystem mostly Gasoline engine cars are used by the public which have high cost of refueling that cause
emission of greenhouse gases. In this way to reduce the automotive emission which causes the Greenhouse gases, an alternative
technology is needed. The most convenient way is to use renewable energy sources. In this scenario we provide an alternative
fuel for vehicles by using renewable energy resources. Among the various resources available, we design a solar power
controlled motor device for automotive application.
In this project, the Electric Car is the way to alter the energy source. Sunlight is the main source of energy to obtain light energy
which is converted into electricity. Amount of energy obtained from the source may vary from time-to-time. The vehicle chosen
for the experiment is Marathi Omni. The solar panels are placed on the top of the car. Energy obtained from the solar panel is
given to the charge controller. Charge controller can prevent overcharging and also the over discharging of the battery. Output
of the charge controller is given to the Valve Regulated Sealed Lead-Acid Battery. Then the Valve Regulated Sealed Lead-Acid
Battery is given to the Motor through Driver circuit which controls speed of the Motor.
Battery charging time through charge controller is four to five hours and the Electric Car travels for the distance of about
150Km with the speed of 30 to 40 Km/hr. Thus the proposed system of automotive vehicle helps to achieve zero pollution, zero
noise effect and fuel consumption

4804 words, including abstract) Abstract. In the United States, most airports do not place any limitations on airline schedules. At a few major airports, the current scheduling restrictions (mostly administrative measures) have not been sufficiently strict to avoid consistent delays and have raised debates about both the efficiency and the fairness of the allocations. With a forecast 1.1 billion yearly air travelers within the U.S. by 2015, airport expansion and technology enhancement alone are not enough to cope with the competition-driven scheduling practices of the airline industry. The policy legacy needs to change to be consistent with airport capacities.

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented e-constraint method, is based on the augmented e-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multiobjective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method.

Voltage security and congestion management are crucial issues in power systems, especially under heavily loaded conditions. In the new scheme of electricity restructuring, voltage security problems become even more serious. Due to the increase in stability margins, FACTS devices are the best option to mitigate voltage instability by reactive power management. The main purpose of this paper is to identify the optimal location and capacity of the Static Synchronous Compensator (STATCOM) to enhance voltage security and identify the capacity of a properly placed IPFC to manage transmission network congestion simultaneously. Artificial intelligence is implemented as a heuristic technique to this complicated constrained optimization problem. The proposed method demonstrates the improvement of the voltage security margin, as well as solving congestion management problems. Significant results through a modified IEEE 14-bus case study show the effectiveness of the proposed algorithm.

A new decomposition method is presented that includes the network through ac modeling within the hydrothermal scheduling optimization process including the losses. In short-term hydrothermal scheduling, the transmission network is typically modeled with dc power flow techniques. Such modeling, however, can lead to impractical solutions when it is verified with ac power flow. Another proposal considers in thermal systems the ac network modeling but not the optimization of losses. The approach presented here addresses issues such as congestion management and control of service quality that often arise in large and weakly meshed networks-the typical pattern of power systems in Latin America. Generalized Benders decomposition and traditional, well-known optimization techniques are used to solve this problem. The master problem stage defines the generation levels by regarding the inter-temporal constraints, whereas the subproblem stage determines both the active and the reactive economical dispatches for each time interval of the load curve. It meets the electrical constraints through a modified ac optimal power flow (OPF). Another important contribution is the inclusion of accelerating techniques aimed at reducing the number of iterations and CPU time. The methodology was proven in a real system and test systems. Results are discussed in this paper.

This article surveys the literature and publicly available information on market power monitoring in electricity wholesale markets. After briefly reviewing definitions, strategies and methods of mitigating market power we examine the various methods of detecting market power that have been employed by academics and market monitors/regulators. These techniques include structural and behavioural indices and analysis as well as various simulation approaches. The applications of these tools range from spot market mitigation and congestion management through to long-term market design assessment and merger decisions. Various market-power monitoring units already track market behaviour and produce indices. Our survey shows that these units collect a large amount of data from various market participants and we identify the crucial role of the transmission system operators with their access to dispatch and system information. Easily accessible and comprehensive data support effective market power monitoring and facilitate market design evaluation. The discretion required for effective market monitoring is facilitated by institutional independence. JEL: D43, L13, L51, L94

As the Internet owes its scalability and stability to TCP, congestion control also plays a key role in the performance, efficiency, and stability of datacenters, as evidenced by the efforts to standardize congestion management (CM) for 10+ Gbps networks. The next step up from datacenters is CM for clouds. New solutions are necessary for clouds because, while the recent CM schemes have been tested on L2 reliable networks, they are limited by design to relatively small single domain datacenters. TCP, on the other hand, while scalable and continously evolving, was not designed for µs-latency lossless networks. To address this problem we first investigate whether path delay could serve as a reliable congestion measure for clouds spanning across multiple datacenters and heterogenous networks. A qualitative open loop analysis of two datacenter networks (10 Gb/s Ethernet and 12x Infiniband) yields positive results. We close the congestion control loop by adapting the delay observer to a re-designed AIMD controller whose base algorithm we extensively analyzed in IEEE 802.1Qau. However, despite the statistical correlation between congestion severity and delay, the low signal/noise ratio (1-2dB), and the congestion notification lag threaten the closed loop stability. Hence we design a combination of two original filters, called Dual-Edge KDS-CUSUM. After preliminary loop tuning in Matlab, simulation results in OMNeT++ bear out the trade-off between stability and dynamic response. The concept is validated on a cluster software implementation.

We describe a new approach to modelling road traffic assignment, code-named DRACULA (Dynamic Route Assignment Combining User Learning and microsimulAtion), in which the emphasis is on the "microsimulation" of individual trip makers and individual vehicles. It represents directly driver choices as they evolve from day to day combined with a detailed within-day traffic simulation model of the space-time trajectories of individual vehicles according to car-following, lane-changing rules and intersection regulations. It therefore models both day-to-day and within day variability in both demand and supply. As such we believe it is a particularly suitable framework for the realistic modelling of "real-time" technological strategies such as responsive traffic control, dynamic route guidance and congestion management strategies.

The paper presents a methodology to quantify the benefits, in terms of monetary values, of FACTS devices when used in deregulated electricity market for congestion management. The proposed methodology is used in the proposal for investment recovery of FACTS devices. Despite the long history of development and mature technology, the practical installations of FACTS devices are still limited. The main reasons for few installations are high investment cost and lack of viable measures to quantify the long list of benefits offered by FACTS device. In this respect, the methodology proposed in the paper provides a promising solution. The proposed methodology is based on establishing pricing schemes with and without FACTS devices using OPF formulation. The volume of market with FACTS devices and the increase in surplus due to them forms the basis of quantifying their benefits. The pricing scheme does not destroy the incentive effect in short run and also makes possible the provision of merchant FACTS. The proposed concept was tested and validated with TCSC in five-bus test system. Result shows that, when TCSC is used to relieve congestion in the system and the investment on TCSC can be recovered.

As the Internet owes its scalability and stability to TCP, congestion control also plays a key role in the performance, efficiency, and stability of datacenters, as evidenced by the efforts to standardize congestion management (CM) for 10+ Gbps networks. The next step up from datacenters is CM for clouds. New solutions are necessary for clouds because, while the recent CM schemes have been tested on L2 reliable networks, they are limited by design to relatively small single domain datacenters. TCP, on the other hand, while scalable and continously evolving, was not designed for µs-latency lossless networks. To address this problem we first investigate whether path delay could serve as a reliable congestion measure for clouds spanning across multiple datacenters and heterogenous networks. A qualitative open loop analysis of two datacenter networks (10 Gb/s Ethernet and 12x Infiniband) yields positive results. We close the congestion control loop by adapting the delay observer to a re-designed AIMD controller whose base algorithm we extensively analyzed in IEEE 802.1Qau. However, despite the statistical correlation between congestion severity and delay, the low signal/noise ratio (1-2dB), and the congestion notification lag threaten the closed loop stability. Hence we design a combination of two original filters, called Dual-Edge KDS-CUSUM. After preliminary loop tuning in Matlab, simulation results in OMNeT++ bear out the trade-off between stability and dynamic response. The concept is validated on a cluster software implementation.

The real-time voltage stability analysis (RTVSA) project is slated to be a part of the suite of computational tools needed for congestion management in the California ISO (CAISO). This is because the problems of voltage stability will be magnified by the effects of multi-transfers through the network. These simultaneous transfers are manifest due to the buying and selling of electric power when the point of production and the point of delivery may be in geographically distant locations in the CAISO control area. The voltage analysis methods described here include the development of advanced functions such as the exact calculation of point of collapse (PoC) using continuation power flow, the identification of weak elements, the automatic selection of remedial actions and the development of operating nomograms. The RTVSA tool will be integrated with EMS and use results from the state estimator. The RTVSA application is scheduled to go online in the 3rd quarter of 2008.

Government and policy makers today face the pressing problem of managing road congestion in urban areas. This paper details the experience of Singapore and the effort made to curb vehicular increase on the roads using a state-of-the-art regulated Electronic Road Pricing (ERP) scheme. Specifically, the paper looks at the public policy effort to forge ahead with road pricing, prepare the island for extensive congestion management, and cap the on-road vehicular population to ensure a smoother flow of commercial transport and distribution.

This paper proposes a scheme for the management of network congestion in the Internal Electricity Market (IEM) of the European Union. This scheme tries to combine the rigor in the treatment of the energy and transmission capacity transactions with the flexibility and pragmatism that are necessary to make the scheme compatible with the current diversity of regulatory approaches and market structures in the Member States participating in the IEM. First a reference scheme is presented with a complete formulation that jointly deals with the energy and capacity markets. Because of the implementation difficulties of this conceptually ideal approach a more pragmatic scheme is proposed instead. The core of this scheme is an explicit auction mechanism that must be run prior to any short-term (daily, typically) energy markets. In this auction, where only transmission capacity is traded, both bilateral contracts and energy bids to Power Exchanges can participate in order to acquire the capacity that is necessary to carry out their transactions. Some technical issues related to the practical implementation of the proposed approach are also examined; these include market liquidity, the financial or physical nature of the long-term contracts, the potential problems of "slicing" transmission capacity and the allocation of congestion rents. Market power issues are ignored.

With increasing number of cores being integrated on a single die, Network-on-Chips (NoCs) have become the de-facto standard in providing scalable communication backbones for these multi-core chips. NoCs have a significant impact on the system's performance, power and reliability. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. We also propose and evaluate a novel fine-grained frequency tuning scheme where we vary the number of virtual-channels in a router dynamically. As a further optimization to these schemes, we propose a frequency tuning scheme where we tune the frequency of the four ports of a mesh router separately from the local port. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. We also evaluate and analyze the proposed schemes from the point of view of reliability against soft error vulnerability and provide guidelines in choosing the appropriate scheme when reliability is the prime design constraint. Experiments using synthetic workloads on an 8 × 8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. Our analysis shows FreqBoost to be the most robust scheme amongst the three schemes when reliability is a concern.

In the Nordic market, manually activated tertiary control based on bids for upward and downward regulation is used for system balancing. Although a system wide merit order list is used, the resulting regulation is suboptimal because of the congestion and the effect of losses, which are not taken into account. This paper proposes an algorithm for the dispatch of regulation resources based on an incremental DC optimal power flow formulation. The results of this model are compared with today's practice for some cases of up-and downward regulation, and a potential for cost reduction is observed. However, the method requires Automatic Generation Control that is not in use in the system, although it is presently evaluated. Also pricing of regulation is an issue, because Location Marginal Prices probably are unacceptable to market participants.

We apply a merchant transmission model to the trilateral market coupling (TLC) arrangement among the Netherlands, Belgium and France as a generic example, and note that it can be applied to any general market splitting or coupling of Europe's different national power markets. In this merchant framework; the system operator allocates financial transmission rights (FTRs) to investors in transmission expansion based upon their preferences, and revenue adequacy. The independent system operator (ISO) preserves some proxy FTRs to deal with potential negative externalities due to an expansion project. This scheme proves to be capable in providing incentives for investment in transmission expansion projects within TLC areas.

Grid computing is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations [1]. Currently, Grid computing is effectively used in scientific research, oil ...

In a competitive electricity market, where market parties try to maximize their profits, it is necessary to keep an acceptable level of power system security to retain the continuity of electricity services to customers at a reasonable cost. Congestion in a power system is turned up due to network limits. After relieving congestion, the network may be operated with a reduced transient stability margin because of increasing the contribution of risky participants. In this paper, a novel congestion management method based on a new transient stability criterion is introduced. Using the sensitivity of corrected transient stability margin with respect to generations and demands, the proposed method so alleviates the congestion that the network can more retain its transient security compared with earlier methods. The proposed transient stability index is constructed considering the likelihood of credible faults. Indeed, market parties participate by their security-effective bids rather than raw bids. Results of testing the proposed method along with the earlier ones on the New-England test system elaborate the efficiency of the proposed method from the viewpoint of providing a better transient stability margin with a lower security cost.

In the paper, we summarize the results of a project that was motivated by the expiration of the "High Density Rule," which defined the slot controls employed at New York's LaGuardia Airport for more than 30 years. The scope of the project included the analysis of several administrative measures, congestion pricing options and slot auctions. The research output includes a congestion pricing procedure and also the specification of a slot auction mechanism. The research results are based in part on two strategic simulations. These were multi-day events that included the participation of airport operators, most notably the Port Authority of New York and New Jersey, FAA and DOT executives, airline representatives and other members of the air transportation community. The first simulation placed participants in a stressful, high congestion future scenario and then allowed participants to react and problem solve under various administrative measures and congestion pricing options. The second simulation was a mock slot auction in which participants bid on LGA arrival and departure slots for fictitious airlines.

Restructured markets are rarely flawless: the expected price reduction frequently has not been achieved and the system is often operated closer to its limits, with smaller margins on security, reliability and reserve. Thus, it is necessary to find out new resources to reduce price spikes and volatility, to improve transient stability and congestion management, to increase transfer capacity and to provide ancillary services. Loads that can be interrupted and/or controlled may prove to be profitable for each one of these purposes. This paper presents and discusses the potential benefits provided by the so-called interruptible loads (IL) to the electric system. Some simulations have been run on the Italian network and on a test system to show how ILs can be a means of increasing the transfer capacity of interconnection lines and how they can compete with generators as ancillary service providers for primary and secondary P/f regulation.

In electricity markets, congestion issues and the right of the access to the grid are very important, because these factors can condition agent participation in the market. Electricity markets involve a Spot Market resulting in transactions that must be technically managed by a central system operator, namely in what concerns real-time dispatch of generation and transmission resources. The use of the grid implies the payment of tariffs that require the consideration of several factors to be defined. Agents can buy transmission rights and congestion situations must be considered in these tariffs. This paper presents a software tool that calculates Firm Transmission Rights, detects and solves congestion problems and calculates the Locational Marginal Prices. As a final result, this tool calculates how much each agent has to pay for grid use. The paper includes a study case using a nine-bus test network and the analysis of the obtained results.

Bilateral contracts are important risk-hedging instruments constituting a major component in the portfolios held by many electric power market participants. However, bilateral contract negotiation is a complicated process as it involves risk management, strategic bargaining, and multi-market participation. This study analyzes a financial bilateral contract negotiation process between a generation company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation outcomes under various conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and simulation are used to gain insight regarding how these negotiation outcomes systematically vary in response to changes in the participants' risk preferences and price biases.

This paper addresses the congestion management problem avoiding offline transmission capacity limits related to stability. These limits on line power flows are replaced by optimal power flow-related constraints that ensure an appropriate level of security, mainly targeting voltage instabilities, which are the most common source of stability problems. Results from an illustrative case study based on the IEEE 24-bus Reliability Test System are analyzed. Conclusions are duly drawn.

This paper compares two contrasting yet often used electricity market-clearing procedures: i) an auction-based algorithm including congestion management and transmission-loss cost allocation, and ii) an optimal power flow. The auction procedure produces a single-period unit commitment, and hence, can be compared directly to an optimal power flow solution. These algorithms are compared in terms of the economic efficiency of the solution attained, and in terms of cross-subsidies among generators and demands. The purpose of this comparison is to quantify the actual cost to market participants of using a simple, seemingly transparent procedure, such as an auction-based algorithm, versus an integrated but computationally intensive one, such as an optimal power flow.

In this paper the authors discuss the feasibility of a zonal UCTE network model for congestion management. Zonal network model aggregates all nodes within a control zone and substitutes it by one equivalent node; individual cross-border lines are substituted by equivalent one link per border. This model allows derivation of Power Transfer Distribution Factors PTDF matrix that could model the interaction between zonal power injections and cross-border flows, and therefore allow for the implementation of flow-based congestion management models. Index Terms -congestion management, flow-based modelling, zonal model, PTDF.

Express buses operating on freeways and arterials are an important part ofmany metropolitan area transit services. Aregional express bus system plan for the San Francisco, California, Bay Area Is presented; network, st. ..tion and stop, operations, and user Issues are addressed. Previous work had focused on high-occupancy vehicles on freeways; the cnrrent work expands the vision of the system to address the door-to-door trip and ser vice on arterials. Gaps in the high·occupancy-vehicle network, priority treatment strategies, access modes and connections, station and stop design, park·and-ride lots, and public and private access services were analyzed, and other user and operator concerns were addressed. The plan was developed through a partnership that included the state transporta tion agency, the metropolitan planning organization, local transportation sales tax and congestion management agencies, and transit operators, with university researchers providing technical support and facilitation of meetings. On·board surveys and consumer focus groups were con ducted along with stakeholder interviews, field studIes, design evalua tions, and simulation modeling. The plan not only produced valuable information for future improvements but also helped develop a high level of cooperation and consensus among the participating agencies. Both methods used and lessons learned from the Bay Area experience should be useful to other metropolitan areas and transit operators.

This paper proposes a novel methodology to identify congestion problems under both "traditional" and "new" uncertainties such as generation costs, location and size of new generators, retirement of old ones, generation patterns, etc. The methodology allows not only identifying the transmission paths and corridors which will have congestion problems, but also the scenarios producing these critical situations. Thus, it can be used not only to simplify the study of new investments (reinforcement of existing lines), but also to facilitate the evaluation of hedging strategies and the design of proactive policies to avoid the detected congestion.

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented e-constraint method, is based on the augmented e-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multiobjective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method.

In recent years, Intelligent Transportation Systems (ITS) have gained increased attention. ITS [1] [2] provide opportunities for traffic engineers and decision-makers to deal with problems related to highway traffic operation and congestion management. This paper deals with building a device for collecting data about Doha traffic and storing this data on a central server. The server maintains a traffic information database that can be used for traffic analysis and prediction.

Summary form only given. This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives (1) alleviation of overload and (2) minimization of cost of operation are optimized to provide Pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect.

Congestion management is one of the most 1 important functions of independent system operator ( ISO ) in 2 the restructured power system. This paper presents two new 3 methodologies for optimal sitting and sizing of distributed 4 generations ( DGs ) in the restructured power systems for 5 congestion management. The proposed methodologies are 6 based upon locational marginal price ( LMP ) and conges-7 tion rent that forms a priority list of candidate buses to reduce 8 the solution space. The proposed priority list facilitates the 9 optimal placement as well as the level of output power of 10 DGs. The proposed methods are implemented on the IEEE 11 14-bus and IEEE 57-bus test systems to illustrate their effec-12 tiveness. An economic consideration of DG placement and 13 its operation is also studied. Simulation studies and results 14 analysis show that the proposed methodologies are capable 15 of finding the best location and optimal size for DGs, which 16 can alleviate congestion in transmission systems.

In both vertically integrated and restructured power systems, it is desired to transmit power to every parts of the network without any limits resulted from congestion and consequently avoiding inefficiency in generation dispatch. In this paper, in order to eliminate power system's congestion, distributed generation (DG) is employed. DG's have nonlinear impacts on the power system characteristics such as, the power transmitted between two locations of the network. Hence for identifying the impacts of DG on system variables, the problem of DG's sizing and placement is formulated as an AC optimal power flow (ACOPF) with binary variables and is solved by using mixed integer programming (MIP). 1 In order to test the feasibility of mentioned method, the IEEE-14 bus systems has been used and also to study the impacts of DG's on this system, a number of system variables have been studied including: generation cost, congestion rent, locational marginal price (LMP), voltage profile and transmission losses.

TodayÕs standard TCP implementations perform congestion control separately for each connection of an end system. It is advantageous in terms of overall performance, e.g., throughput and (short-term) fairness, to share network information between several TCP connections of an end system by establishing a common congestion control.

Given declining resources, pressing problems, and environmental constraints, state departments of transportation (DOTs) are increasingly motivated to manage peak demand of vehicle trips as a way to mitigate congestion and improve overall performance of the highway system ...

A traffic simulation and assignment model is used to compute the social costs of congestion in five English towns, together with potential road charges. The social costs of congestion are measured by the deadweight loss and computed as the area between the marginal social cost and inverse demand curves between the actual and efficient levels of traffic. Potential road charges

EU Member States are pursuing large scale investment in renewable generation in order to meet a 2020 target to source 20% of total energy sources by renewables. As the location for this new generation differs from the location of existing generation sources, and is often on the extremities of the electricity network, it will create new flow patterns and transmission needs. While congestion exists between European countries, increasing the penetration of variable sources of energy will change the current cross-border congestion profile. It becomes increasingly important for the power market design to foster the full use of existing transmission capacity and allow for robust operation even in the presence of system congestion. After identifying five criteria that an effective congestion management scheme for European countries will need, this paper critically assess to what extent the various approaches satisfy the requirements.

With the introduction of the Power Exchange, one of the most critical issues to be faced by a Transmission System Operator (TSO) is to take into account the transmission constraints in a simplified market model. The zonal approach represents a suitable solution, since its mechanism can be easily understood by all the operators; on the other side, it requires to establish a priori the relevant transmission constraints. However, in a meshed network, this solution results in some problems in the management of the system, mainly because the Transmission Capability (TTC) value is deeply influenced by both demand and generation patterns. In order to face this problem, coupling the clearing process with an on-line TTC evaluation tool would represent the best solution, allowing the full exploitation of the transmission facilities. Since all the methods already proposed in the technical literature are not suitable for on-line applications due to their huge computation time, a new approach is proposed. An Artificial Neural Network (ANN) is used to estimate the TTC in real time: once the proposed model has been trained, it is adopted for a real time update of the TTC between two market areas, with respect to the actual market results, in order to increase the market efficiency and to reduce the associated congestion costs. Copyright © 2009 John Wiley & Sons, Ltd.

In queueing networks with blocking, stations wishing to transmit customers to a full queue are blocked and need to take alternative action on completing a service. In general, productforms, i.e. separable solutions for such a network's equilibrium state probabilities, do not exist but some productforms have been obtained over the years in special cases, using a variety of techniques. We show that the Reversed Compound Agent Theorem (RCAT) can obtain these diverse results in a uniform way by its direct application, so unifying product-forms in networks with and without blocking. New product-forms are also constructed for a type of blocking we call 'skipping', where a blocked station sends its output-customers to the queue after the one causing the blocking in that customer's path. Finally, we investigate a novel congestion management scheme for networks of finitecapacity queues in which a station with a full queue transmits signals that delete customers from upstream queues in order to reduce incoming traffic.

Virtualized datacenters strive to reduce costs through workload consolidation. Workloads exhibit a diverse set of IO behaviors and varying IO load that makes it difficult to estimate the IO performance on shared storage. As a result, system administrators often resort to gross overprovisioning or static partitioning of storage to meet application demands. In this paper, we introduce Pesto, a unified storage performance management system for heterogeneous virtualized datacenters. Pesto is the first system that completely automates storage performance management for virtualized datacenters, providing IO load balancing with cost-benefit analysis, perdevice congestion management, and initial placement of new workloads.

Abstract: Weaknesses in several recently proposed ideas about congestion control and avoidance in high-speed netwroks are identified. Both sides of the debate concerning prior reservation of resources versus walk-in service, open-loop control versus feedback control, rate control versus window control, and router-based control versus source-based control are presented. The circumstances under which backpressure is useful or not are discussed, and it is argued that a single congestion scheme is not sufficient, but that a combination of ...

This paper proposes two approaches for a unified management of congestions due to voltage instability and thermal overload in a deregulated environment. Both techniques aim to remove, in some optimal manner, voltage and thermal congestions stemming from base case or post-contingency states, by a simultaneous handling of operating and security constrains with respect to several contingencies. The objective of the first approach is to adjust the market-based power injections (generator output and possibly load consumption) at the least cost while the second one aims at curtailing power transactions in a transparent and non-discriminatory way. These techniques rely on sensitivities which pinpoint the best remedial actions against congestions owing to voltage instability and thermal overload. Numerical results with both approaches are provided on a realistic 80-bus system model.

Extant research into traffic congestion in relation to freight transportation and logistics is usually conducted in the context of economies (e.g. he USA) where road traffic congestion in a single region (e.g. Los Angeles County) will have negligible overall impact on supply chains. The Auckland region in New Zealand (NZ) represents an exception: it accounts for a third of NZ’s