Methods of Congestion Control for Adaptive Continuous Media

Since the first exchange of data between machines in different locations in early 1960s, computer networks have grown exponentially with millions of people now using the Internet. With this, there has also been a rapid increase in different kinds of services offered over the World Wide Web from simple e-mails to streaming video. It is generally accepted that the commonly used protocol suite TCP/IP alone is not adequate for a number of modern applications with high bandwidth and minimal delay requirements. Many technologies are emerging such as IPv6, Diffserv, Intserv etc, which aim to replace the onesize-fits-all approach of the current lPv4. There is a consensus that the networks will have to be capable of multi-service and will have to isolate different classes of traffic through bandwidth partitioning such that, for example, low priority best-effort traffic does not cause delay for high priority video traffic. However, this research identifies that even within a class there may be delays or losses due to congestion and the problem will require different solutions in different classes. The focus of this research is on the requirements of the adaptive continuous media class. These are traffic flows that require a good Quality of Service but are also able to adapt to the network conditions by accepting some degradation in quality. It is potentially the most flexible traffic class and therefore, one of the most useful types for an increasing number of applications. This thesis discusses the QoS requirements of adaptive continuous media and identifies an ideal feedback based control system that would be suitable for this class. A number of current methods of congestion control have been investigated and two methods that have been shown to be successful with data traffic have been evaluated to ascertain if they could be adapted for adaptive continuous media. A novel method of control based on percentile monitoring of the queue occupancy is then proposed and developed. Simulation results demonstrate that the percentile monitoring based method is more appropriate to this type of flow. The problem of congestion control at aggregating nodes of the network hierarchy, where thousands of adaptive flows may be aggregated to a single flow, is then considered. A unique method of pricing mean and variance is developed such that each individual flow is charged fairly for its contribution to the congestion

Models for Iterative Multiattribute Procurement Auctions

Multiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods. This paper provides an iterative auction design for an important special case of the multiattribute allocation problem with special (preferential independent) additive structure on the buyer value and seller costs. Auction Additive&Discrete provides a refined design for a price-based auction in which the price feedback decomposes to an additive part with a price for each attribute and an aggregate part that appears as a price discount for each supplier. In addition, this design also has excellent information revelation properties that are validated through computational experiments. The auction terminates with an outcome of a modified Vickrey-Clarke-Groves mechanism. This paper also develops Auction NonLinear&Discrete for the more general nonlinear case-a particularly simple design that solves the general multiattribute allocation problem, but requires that the auctioneer maintains prices on bundles of attribute levels.Engineering and Applied Science

Price-based optimal control of electrical power systems

During the past decade, electrical power systems have been going through some major restructuring processes. From monopolistic, highly regulated and one utility controlled operation, a system is being restructured to include many parties competing for energy production and consumption, and for provision of many of the ancillary services necessary for system operation. With the emergence of competitive markets as central operational mechanisms, the prime operational objective has shifted from a centralized, utility cost minimization objective to decentralized, profit maximization objectives of competing parties. The market-based (price-based) operation is shown to be practically the only approach that is capable to simultaneously provide incentives to hold the prices at marginal costs and to minimize the costs. As a result, such an operational structure inherently tends to maximize the social welfare of the system during its operation, and to accelerate developments and applications of new technologies. Another major change that is taking place in today’s power systems is an increasing integration of small-scale distributed generation (DG) units. Since in future power systems, a large amounts of DG will be based on renewable, intermittent energy sources, e.g. wind and sun, these systems will be characterized by significantly larger uncertainties than those of the present power systems. Power markets significantly deviate from standard economics since the demand side is largely disconnected from the market, i.e. it is not price responsive, and it exhibits uncertain, stochastic behavior. Furthermore, since electrical energy cannot be efficiently stored in large quantities, production has to meet these rapidly changing demands in real-time. In future power systems, efficient real-time power balancing schemes will become crucial and even more challenging due to the significant increase of uncertainties by large-scale integration of renewable sources. Physical and security limits on the maximal power flows in the lines of power transmission networks represent crucial system constraints, which must be satisfied to protect the integrity of the system. Creating an efficient congestion management scheme for dealing with these constraints is one of the toughest problems in the electricity market design, as the line power flows are characterized by complex dependencies on nodal power injections. Efficient congestion control has to account for those limits by adequately transforming them into market signals, i.e. into electricity prices. One of the main contributions of this thesis is the development of a novel dynamic, distributed feedback control scheme for optimal real-time update of electricity prices. The developed controller (which is called the KKT controller in the thesis) reacts on the network frequency deviation as a measure of power imbalance in the system and on measured violations of line flow limits in a transmission network. The output of the controller is a vector of nodal prices. Each producer/consumer in the system is allowed to autonomously react on the announced price by adjusting its production/consumption level to maximize its own benefit. Under the hypothesis of global asymptotic stability of the closed-loop system, the developed control scheme is proven to continuously balance the system by driving it towards the equilibrium where the transmission power flow constraints are satisfied, and where the total social welfare of the system is maximized. One of the advantageous features of the developed control scheme is that, to achieve this goal, it requires no knowledge of marginal cost/benefit functions of producers/consumers in the system (neither is it based on the estimates of those functions). The only system parameters that are explicitly included in the control law are the transmission network parameters, i.e. network topology and line impedances. Furthermore, the developed control law can be implemented in a distributed fashion. More precisely, it can be implemented through a set of nodal controllers, where one nodal controller (NC) is assigned to each node in the network. Each NC acts only on locally available information, i.e. on the measurements from the corresponding node and on the information obtained from NC’s of the adjacent nodes. The communication network graph among NC’s is therefore the same as the graph of the underlying physical network. Any change is the network topology requires only simple adjustments in NC’s that are local to the location of the change. To impose the hard constraints on the level to which the transmission network lines are overloaded during the transient periods following relatively large power imbalances in the system, a novel price-based hybrid model predictive control (MPC) scheme has been developed. The MPC control action adds corrective signals to the output of the KKT controller, i.e. to the nodal prices, and acts only when the predictions indicate that the imposed hard constraint will be violated. In any other case, output of the MPC controller is zero and only the KKT controller is active. Under certain hypothesis, recursive feasibility and asymptotic stability of the closed-loop system with the hybrid MPC controller are proven. Next contribution of this thesis is formulation of the autonomous power networks concept as a multilayered operational structure of future power systems, which allows for efficient large-scale integration of DG and smallscale consumers into power and ancillary service markets, i.e. markets for different classes of reserve capacities. An autonomous power network (AN) is an aggregation of networked producers and consumers, whose operation is coordinated/controlled with one central unit (AN market agent). By performing optimal dispatching and unit commitment services, the main goals of an AN market agent is to efficiently deploy the AN’s internal resources by its active involvement in power and ancillary service markets, and to optimally account for the local reliability needs. An autonomous power network is further defined as a major building block of power system operation, which is capable of keeping track of its contribution to the uncertainty in the overall system, and is capable of bearing the responsibility for it. With the introduction of such entities, the conditions are created that allow for the emergence of novel, competitive ancillary service market structures. More precisely, in ANs based power systems, each AN can be both producer and consumer of ancillary services, and ancillary service markets are characterized by double-sided competition, what is in contrast to today’s single-sided ancillary service markets. One of the main implications of this novel operational structure in that, by facilitating competition, it creates the strong incentive for ANs to reduce the uncertainties and to increase reliability of the system. On a more technical side, the AN concept is seen as decentralization and modularization approach for dealing with the future, large scale, complex power systems. As additional contribution of this thesis, motivated by the KKT controller for price-based real-time power balancing and congestion management, the general KKT control paradigm is presented in some detail. The developed control design procedure presents a solution to the problem of regulating a general linear time-invariant dynamical system to a time-varying economically optimal operating point. The system is characterized with a set of exogenous inputs as an abstraction of time-varying loads and disturbances. Economic optimality is defined through a constrained convex optimization problem with a set of system states as decision variables, and with the values of exogenous inputs as parameters in the optimization problem. A KKT controller belongs to a class of dynamic complementarity systems, which has been recently introduced and which has, due to its wide applicability and specific structural properties, gained a significant attention in systems and control community. The results of this thesis add to the list of applications of complementarity systems in control

Distributed Market-Grid Coupling Using Model Predictive Control

In this dissertation, a feedback control concept is proposed for modeling a market-grid coupling. The contributions are fourfold: 1) Identification and characterization of an interoperable control between the power market and the power grid; 2) Design of a closed-loop MPC for the market-grid coupling; 3) Extension of the single control loop with a collaborative distributed MPC strategy for coupling distributed markets and grids; 4) Development of an adaptive load forecasting framework

TUBE: Time-dependent pricing for mobile data

The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an autopilot mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage

Models for iterative multiattribute procurement auctions

informs ® doi 10.1287/mnsc.1040.0340 © 2005 INFORMS Multiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods. This paper provides an iterative auction design for an important special case of the multiattribute allocation problem with special (preferential independent) additive structure on the buyer value and seller costs. Auction Additive&Discrete provides a refined design for a price-based auction in which the price feedback decomposes to an additive part with a price for each attribute and an aggregate part that appears as a price discount for each supplier. In addition, this design also has excellent information revelation properties that are validated through computational experiments. The auction terminates with an outcome of a modified Vickrey-Clarke-Groves mechanism. This paper also develops Auction NonLinear&Discrete for the more general nonlinear case—a particularly simple design that solves the general multiattribute allocation problem, but requires that the auctioneer maintains prices on bundles of attribute levels. Key words: multiattribute negotiation; iterative auctions; price-based feedback; Vickrey-Clarke-Groves mechanism; ex post Nash equilibrium; straightforward bidding; procurement History: Accepted by G. Anandalingam and S. Raghavan, special issue editors; received June 6, 2002. This paper was with the authors 8 months for 3 revisions

Uncertainty-Aware Transactive Operation Decisions for Grid-Friendly Building Clusters

In this thesis, the emerging local energy transaction of prosumer (building) at distribution level is focused. Decision models and efficient algorithms are developed to study the collaborative energy transaction decisions of building clusters in three research phases. Number of research has demonstrated that building clusters can achieve more benefits like lower total energy cost, however, some buildings have to make sacrifices of their own interests for collective interests for the clusters. To motivate individual buildings, we propose four different transactive energy management models in first phase where each building is allowed to have energy transaction with others while individual requirement has to be satisfied. The first model focuses on maximizing collective interests and this model is appropriate when all the buildings are operated by one manager, both collective and individual interests are considered in second model which is suitable when different buildings have heterogeneous individual interests. The third and fourth models aim to maximize both collective and individual interests, this two models are preferred when buildings have homogeneous individual interests (e.g. same saving percentage or absolute saving amount). Then next, in second phase, large scale (e.g. community level) building clusters is studied. To enable more efficient transactive operations among prosumers, we propose a swarm intelligence based bi-level distributed decision approach. Particle swarm optimizer is employed at system level to coordinate all the buildings to dispatch shared energy while each building at sub-system level will employ a mixed integer operating model to obtain operation decisions for its energy systems, such as distributed generators and storage systems. For the purpose of accelerating convergence of swarm algorithm, a marginal price based feedback strategy is proposed. During each iteration, each building will solve its local decision model, the marginal prices for exchanged energy will be collected and fed back to system level to guide velocity and position updating of particle swarm. Proposed distributed approach is applied on distributed control for building-charging station integration as a case study, and then it is evaluated in terms of accuracy, scalability and robustness. It is demonstrated that proposed approach is very computationally efficient, scalable and robust, and the computational complexity if O(n) where n is the number of buildings in the cluster. To deal with uncertain information about electricity load and solar radiation, scenario-based centralized two-stage stochastic operation model is firstly established at third research phase, where electric storage and power generating unit are assumed to provide different kinds of operating reserves in ancillary market. Proposed swarm intelligence based distributed decision framework and coordination algorithm from previous phase are extended to incorporate with stochastic programming. In order to further decrease model complexity of planning optimization and utilize updated information, model prediction control approach is embedded in proposed energy transaction process to make online decisions. In summary, this thesis has proposed a swarm intelligence based methodology of coordinating buildings' transactive operation at distribution level. The main idea is to utilize marginal information from individual optimization to allocate resources more effectively for collective optimality. This methodology could be adopted for more applications, such as robots swarm coordination, etc. There are, however, several issues that could be addressed in future investigations. For example, only electricity transaction is allowed in research phase II and III, multiple transacted energy resources (heating, cooling and electricity) will be considered, and the correlation between different kinds of energy resources will be emphasized. In addition, the energy transaction price of local transaction market is assumed based on transparent information in research phase I. Pricing negotiation mechanism will be worth developed based on game theory to optimally determine local energy transaction price. More broadly, from system perspective, uncertainty coupling and propagation from different sources may have great impacts on the algorithm performance, also communication between system level and subsystem agents may be delayed and missing, therefore distributed coordination algorithm should be robust when facing with such unexpected conditions in practice