Transmission network expansion planning considering unit commitment problem simultaneously

Transmission Network Expansion Planning (TNEP) is an important part of power system planning in new structured power market. Its goal is to minimize the network construction and operational cost while satisfying the demand increase, considering technical and economic conditions. Since change in Unit Commitment (UC), influences transmission lines, this paper presents an Integer Coded Genetic Algorithm (ICGA) to solve both problems together. Genetic algorithm can consider all generation and network constraints. Also random behavior of genetic algorithm can simulate real probabilities such as uncertainty in generation. Considering uncertainty for some units, in each iteration, it can find out the probability of congestion for each line. After all iterations it can highlight the transmission lines which need expansion, because of high congestion probability. Simulation results of the proposed idea are presented for IEEE30-bus network

Congestion Control in a Reliable Scalable Message-Oriented Middleware

This paper presents congestion control mechanisms for reliable and scalable message-oriented middleware following the publish/subscribe communication model. We identify the key requirements of congestion control in this environment, how it differs from congestion control for the Internet, and propose a combination of two congestion control mechanisms, (1) driven by a publisher hosting broker (PDCC), (2) driven by a subscriber hosting broker (SDCC). SDCC decouples the notion of a receive window and a NACK window, and is used by subscriber hosting brokers in recovery mode. PDCC implements a scalable and low latency feedback loop between a publisher hosting broker and all subscriber hosting brokers, which is used to adjust the rate of publishing new messages, to allow brokers in recovery to eventually catch up, and other brokers to keep up. We present a detailed experimental evaluation of our implementation of these mechanisms in the Gryphon system by injecting network failures and link congestion

STATISTICAL-ANALYSIS OF THE GENERALIZED PROCESSOR SHARING SCHEDULING DISCIPLINE

In this paper, we develop bounds on the individual session backlog and delay distribution under the Generalized Processor Sharing (GPS) scheduling discipline . This work is motivated by, and is an extension of, Parekh and Gallager 's deterministic study of the GPS scheduling discipline with leaky-bucket token controlled sessions [15], [16]. Using the exponentially bounded burstiness (E.B.B.) process model introduced in [18] as a source traffic characterization, we establish results that extend the deterministic study of GPS: for a single GPS server in isolation, we present statistical bounds on the distributions of backlog and delay for each session. In the network setting, we show that networks belonging to a broad class of GPS assignments, the socalled Consistent Relative Session Treatment (CRST) GPS assignments, are stable in a stochastic sense. In particular, we establish simple bounds on the distribution of backlog and delay for each session in a Rate Proportional Processor Sharin..