No description available.

The Internet has become the preferred transport medium for almost every type of communication, continuing to grow, both in terms of the number of users and delivered services. Efforts have been made to ensure that time sensitive applications receive sufficient resources and subsequently receive an acceptable Quality of Service (QoS). However, typical Internet users no longer use a single service at a given point in time, as they are instead engaged in a multimedia-rich experience, comprising of many different concurrent services. Given the scalability problems raised by the diversity of the users and traffic, in conjunction with their increasing expectations, the task of QoS provisioning can no longer be approached from the perspective of providing priority to specific traffic types over coexisting services; either through explicit resource reservation, or traffic classification using static policies, as is the case with the current approach to QoS provisioning, Differentiated Services (Diffserv). This current use of static resource allocation and traffic shaping methods reveals a distinct lack of synergy between current QoS practices and user activities, thus highlighting a need for a QoS solution reflecting the user services. The aim of this thesis is to investigate and propose a novel QoS architecture, which considers the activities of the user and manages resources from a user-centric perspective. The research begins with a comprehensive examination of existing QoS technologies and mechanisms, arguing that current QoS practises are too static in their configuration and typically give priority to specific individual services rather than considering the user experience. The analysis also reveals the potential threat that unresponsive application traffic presents to coexisting Internet services and QoS efforts, and introduces the requirement for a balance between application QoS and fairness. This thesis proposes a novel architecture, the Congestion Aware Packet Scheduler (CAPS), which manages and controls traffic at the point of service aggregation, in order to optimise the overall QoS of the user experience. The CAPS architecture, in contrast to traditional QoS alternatives, places no predetermined precedence on a specific traffic; instead, it adapts QoS policies to each individual’s Internet traffic profile and dynamically controls the ratio of user services to maintain an optimised QoS experience. The rationale behind this approach was to enable a QoS optimised experience to each Internet user and not just those using preferred services. Furthermore, unresponsive bandwidth intensive applications, such as Peer-to-Peer, are managed fairly while minimising their impact on coexisting services. The CAPS architecture has been validated through extensive simulations with the topologies used replicating the complexity and scale of real-network ISP infrastructures. The results show that for a number of different user-traffic profiles, the proposed approach achieves an improved aggregate QoS for each user when compared with Best effort Internet, Traditional Diffserv and Weighted-RED configurations. Furthermore, the results demonstrate that the proposed architecture not only provides an optimised QoS to the user, irrespective of their traffic profile, but through the avoidance of static resource allocation, can adapt with the Internet user as their use of services change.

While two objectives of deregulation are to reduce service interruptions and achieve lower energy costs, deregulation has actually introduced new problems in both areas. Since the transmission network was built in the last century, mainly for the regulated power systems, with mostly local power transfers, the increased long distance power transfer introduced by free energy trading has made congestion happen more frequently. When congestion happens, service interruptions occur and higher energy costs arise. We approach the issue from the viewpoints of both planning and online operations. Accordingly, we develop a reactive online remedying method that uses Flexible AC Transmission (FACTS) devices to eliminate congestion with minimum transaction curtailment to maintain market force. We also develop a proactive preventive method for offline planning, such as in the day-ahead market, which uses FACTS devices to maximize the Total Transfer Capability so that more transactions can be scheduled without causing congestion in the system. Optimal Power Flow is used for both methods with FACTS devices treated as control variables so that they can be adjusted to the best FACTS parameters to minimize the transaction curtailment or maximize the Total Transfer Capability. We demonstrate that FACTS devices are very effective for both situations. Since the installation of FACTS devices involves heavy infrastructure investment, an effective pricing method needs to be in place to encourage this investment by guaranteeing sufficient return. This research uses a novel pricing scheme to charge for both utilizing the FACTS devices and having the FACTS devices operating at their limits. The owners of the FACTS devices will then be able to recover their investment. With the above control method and the pricing method, we can make better use of the existing transmission network and relieve congestion. However, deregulation may also degrade system reliability since the generation companies are not obligated to sell into the market and market participation is driven by market forces instead. We use the market share based short-term adequacy analysis method to address generation resource adequacy issues. The market share based method uses the market share time series for the generation companies to reflect their market behavior in the new environment. Multiple regression modeling, a tool of time series analysis, is used to model involved factors. We demonstrate how the market share based short-term adequacy analysis method can capture the adequacy problems that the traditional method cannot. In addition, it can also help to remedy the adequacy problems, which can in turn reduce service interruption and thus the energy price.

In this research, the problem of real-time congestion management in a modern distribution system with massive active elements such as electric vehicles (EVs), distributed energy resources (DERs), and demand response (DR) is investigated. A novel hierarchical operation and management framework is proposed that can take advantage of the demand side contribution to manage the real-time congestion. There are five main steps in this framework as 1) the aggregators send their demand to the microgrid operators (MGOs), 2) the MGOs send their demand to the distribution system operator (DSO), 3) the DSO detects the congestions and calls the engaged MGOs to reduce their demand, 4) the MGOs update the electricity price to motivate the aggregators to reduce the overall demand, and 5) the DSO dispatches the system according to the finalized demand. The proposed framework is validated on two modified IEEE unbalanced test systems. The results illustrate two congestion cases at t=8:45 am and t=9:30 am in the modified IEEE 13-bus test system, which needs 363kW and 286 kW load reductions, respectively, to be fully addressed. MG#1 and MG#2 are engaged to maintain the 363 kW reduction at t=8:45, and MG#3 and MG#4 are called to reduce their demands by 386 kW at t=9:30 am. The overall interactions can relieve the congested branches. The DSO’s calculations show three congestions at t=1 pm, t=3 pm, and t=9 pm on the IEEE 123-bus test system. These congestion cases can be alleviated by reducing 809 kW, 1177 kW, and 497 kW from the corresponding MGs at t=1 pm, t=3 pm, and t=9 pm, respectively. The second part of the simulation results demonstrates that the proposed real-time data estimator (RDE) can reduce the DSO’s miss-detected congestion cases due to the uncertain data. There are two miss-detected congestions in the IEEE 13-bus test system at t=1:15 pm and t=1:30 pm that can be filtered for t=1:15 pm and minored for t=1:30 pm using the RDE. The proposed RDE can also reduce the miss-detected congestions from 18 cases to four cases in the IEEE 123-bus test system. As a result, the RDE can minimize the extra costs due to the uncertain data. The overall results validate that the proposed framework can adaptively manage real-time congestions in distribution systems.

This thesis describes research into the problem of transmission congestion management. The causes, remedies, pricing methods, and other issues of transmission congestion are briefly reviewed. This research is to develop market-based approaches to cope with transmission congestion in real-time, short-run and long-run efficiently, economically and fairly. Extended OPF techniques have been playing key roles in many aspects of electricity markets. The Primal-Dual Interior Point Linear Programming and Quadratic Programming are applied to solve various optimization problems of congestion management proposed in the thesis. A coordinated real-time optimal dispatch method for unbundled electricity markets is proposed for system balancing and congestion management. With this method, almost all the possible resources in different electricity markets, including operating reserves and bilateral transactions, can be used to eliminate the real-time congestion according to their bids into the balancing market. Spot pricing theory is applied to real-time congestion pricing. Under the same framework, a Lagrangian Relaxation based region decomposition OPF algorithm is presented to deal with the problems of real-time active power congestion management across multiple regions. The inter/intra-regional congestion can be relieved without exchanging any information between regional ISOs but the Lagrangian Multipliers. In day-ahead spot market, a new optimal dispatch method is proposed for congestion and price risk management, particularly for bilateral transaction curtailment. Individual revenue adequacy constraints, which include payments from financial instruments, are involved in the original dispatch problem. An iterative procedure is applied to solve this special optimization problem with both primal and dual variables involved in its constraints. An optimal Financial Transmission Rights (FTR) auction model is presented as an approach to the long-term congestion management. Two types of series F ACTS devices are incorporated into this auction problem using the Power Injection Model to maximize the auction revenue. Some new treatment has been done on TCSC's operating limits to keep the auction problem linear.

One of the main concerns with the introduction of competition in the power sector is the strategic behaviour of market participants. Computable models of strategic behaviour are becoming increasingly important to understand the complexities of competition. Such models can help analyze market designs and regulatory policies. In this thesis, further developments on the modelling and analysis of strategic behaviour in electricity markets are presented. This thesis work has been conducted along three research lines. <br /><br /> In the first research line, an oligopolistic model of a joint energy and spinning reserve market is formulated to analyze imperfect competition. Strategic behaviour is introduced by means of conjectured functions. With this integrated formulation for imperfect competition, the opportunity cost between generation and spinning reserve has been analytically derived. Besides, inter-temporal and energy constraints, and financial transmission rights are taken into account. Under such considerations, competition in electricity markets is modelled with more realism. The oligopolistic model is formulated as an equilibrium problem in terms of complementarity conditions. <br /><br /> In the second research line, a methodology to screen and mitigate the potential exacerbation of market power due to the ownership of financial transmission rights is presented. Hedging position ratios are computed to quantify the hedging level of financial transmission rights. They are based on the actual impact that each participant has in the energy market, and on the potential impact that it would have with the ownership of financial transmission rights. Thus, hedging position ratios are used to identify the potential gambling positions from the transmission rights bidders, and, therefore, used to prioritize critical positions in the auction for transmission rights. <br ><br /> In the last research line, alternative equilibrium models of markets for financial transmission rights are formulated. The proposed equilibrium framework is more natural and flexible for modelling markets than the classic cost-minimization markets. Different markets for financial transmission rights are modelled, namely: i) forwards, ii) options, and iii) joint forwards and options. Moreover, one-period, multi-period and multi-round markets for forwards are derived. These equilibrium models are proposed to analyze the bidding strategies of market participants. The potential impact of bidders on congestion prices is modelled by means of conjectured transmission price functions.

The road traffic along with other key infrastructure sectors such as telecommunication, power, etc. has an important role in economic and technological growth of one country. Traffic engineers and analysts are responsible for solving a diversity of traffic problems, such as traffic data acquisition and evaluation. In response to the need to improve traffic operation, researchers implement advanced technologies and integration of systems and data, and develop state-of-the-art applications. This thesis introduces three novel web applications with an aim to offer traffic operators, managers, and analysts’ possibility to monitor the congestion, and analyze incidents and signal performance measures. They offer more detailed analysis providing users with insights from different levels and perspectives. The benefit of providing these visualization tools is more efficient estimation of the performance of local networks, thus facilitating the decision making process in case of emergency events. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

This work deals with problems where multiple actors simultaneously take control decisions and implement the corresponding actions in large multi-area power systems. The fact that those actions take place in the same transmission grid introduces a coupling between the various decision-making problems. First, transmission constraints involving all actors' controls must be satisfied, while, second, the satisfaction of an actor's operational objective depends, in general, not only on its own actions but on the others' too. Algorithms and/or operational procedures are, thus, developed seeking to reconcile the multiple actors' simultaneous decisions. The confidentiality and operational autonomy of the actors' decision-making procedures are preserved. In particular, two specific problems leading to such a multi-actor situation have been treated. The first is drawn from a recently emerging situation, at least in Europe, where several Transmission System Operators (TSOs) have installed and/or are planning to install Phase Shifting Transformers (PSTs) in such locations in their areas that, by properly adjusting the PST phase angle settings, they can significantly control the power flows entering and exiting their systems. A general framework is proposed for the control of PSTs owned by several TSOs, taking into account their interactions. The proposed solution is the Nash equilibrium of a sequence of optimizations performed by the various TSOs, each of them taking into account the other TSOs' control settings as well as operating constraints relative to the whole system. The method is applied to a linearized network model and illustrated on the IEEE 118-bus system. The second multi-actor situation dealt with in this work stems from the recently increasing amount of discussions and efforts made towards creating the right market structures and operational practices that would facilitate a seamless inter-area trade of electricity throughout large interconnections. In this respect, in accordance with European Union's goal of a fully functional Internal Electricity Market where ideally every consumer will be able to buy electric energy from every producer all across the interconnection, the possibility of every market participant to place its bid in whatever electricity market of an interconnection has been considered. This results in overlapping markets, each with its own schedule of power injections and withdraws, comprising buses all around the interconnection, that are cleared simultaneously by Transaction Schedulers (TSs). An iterative procedure is proposed to reconcile the various TS schedules such that congestion is managed in a fair and efficient way. The procedure converges to such schedules that the various TS market clearings are in a Nash equilibrium. The method is then extended towards several directions: enabling market participants to place their bids simultaneously in more than one TS's market, incorporating $N-1$ security constraints, allowing for joint energy-reserve dispatch, and, accounting for transmission losses. The corresponding iterative algorithms are thoroughly illustrated in detail on a 15-bus as well as the IEEE RTS-96 system.

One of the main concerns with the introduction of competition in the power sector is the strategic behaviour of market participants. Computable models of strategic behaviour are becoming increasingly important to understand the complexities of competition. Such models can help analyze market designs and regulatory policies. In this thesis, further developments on the modelling and analysis of strategic behaviour in electricity markets are presented. This thesis work has been conducted along three research lines. <br /><br /> In the first research line, an oligopolistic model of a joint energy and spinning reserve market is formulated to analyze imperfect competition. Strategic behaviour is introduced by means of conjectured functions. With this integrated formulation for imperfect competition, the opportunity cost between generation and spinning reserve has been analytically derived. Besides, inter-temporal and energy constraints, and financial transmission rights are taken into account. Under such considerations, competition in electricity markets is modelled with more realism. The oligopolistic model is formulated as an equilibrium problem in terms of complementarity conditions. <br /><br /> In the second research line, a methodology to screen and mitigate the potential exacerbation of market power due to the ownership of financial transmission rights is presented. Hedging position ratios are computed to quantify the hedging level of financial transmission rights. They are based on the actual impact that each participant has in the energy market, and on the potential impact that it would have with the ownership of financial transmission rights. Thus, hedging position ratios are used to identify the potential gambling positions from the transmission rights bidders, and, therefore, used to prioritize critical positions in the auction for transmission rights. <br ><br /> In the last research line, alternative equilibrium models of markets for financial transmission rights are formulated. The proposed equilibrium framework is more natural and flexible for modelling markets than the classic cost-minimization markets. Different markets for financial transmission rights are modelled, namely: i) forwards, ii) options, and iii) joint forwards and options. Moreover, one-period, multi-period and multi-round markets for forwards are derived. These equilibrium models are proposed to analyze the bidding strategies of market participants. The potential impact of bidders on congestion prices is modelled by means of conjectured transmission price functions.

The ¡§Congestion Management¡¨ (CM) always has been an outstanding and major problem in power system operation. To solve this problem, experts compose solutions in a wide variety. This thesis, based on the equivalent current, applies the Equivalent Current Injection (ECI) concept and circuit parameters to derive the Power Flow Tracing Method (PFTM) . By means of this method we can get a Sensitive Matrix (SM), which is also called the Contribution Matrix (CM), to show the linear relationship between the input power and tidal current discharge of each generator set, with the linear relationship we can derive the mathematic model of treating the congestion problem discussed in this thesis. Combining the Predictor-Corrector Interior Point Algorithm (PCIPA), we can manipulate the change of each generator set in the prospective of solving the congestion problem resulting from the system contingency (SC). The thesis performed various simulations for the IEEE 30 Bus system. Regarding the power contingencies, the solutions of the power-congestion problems can be resulted from the following incidents: heavy load addition, transmission line tripped, generator malfunction as well as the multi-contingencies, etc., which can all be solved with solutions within reasonably restricted domains. We can thus verify the effectiveness of the method .