Power Sharing, Tracing And Prediction Of Losses For Deregulated Operation Of Power Systems
| dc.contributor.author | Nallagownden, Perumal | |
| dc.date.accessioned | 2018-07-31T06:53:55Z | |
| dc.date.available | 2018-07-31T06:53:55Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | Fully deregulated energy market consists of a number of generation providers, a number of transmission system operators, and a number of retailers. The capability to predict the contribution of each generator to a retailer’s demand, the power loss in the transaction, the line losses associated with the transaction are necessary to frame transaction and transmission service hiring contracts. A comparative study of the different loss allocation methods namely Pro rata, Incremental allocation and Proportional sharing was carried out on the 14-bus, 24-bus IEEE RTS system, and from the results it was concluded that the proportional sharing method is the most suitable method to be used in this research. Proportional sharing based on regression and power tracing method using linear equations, was used to determine the different transactions to supply a specific retailer’s demand, and losses related to each transaction. The learning coefficients from a generalized quadratic relationship and regression method using the inputs from the current and past operating scenarios is used to predict a generator’s contribution to a retailer’s demand, power loss for this transaction, share of transactions in a line at the retailers end and their associated line losses, for an oncoming operating scenario. Prediction of power loss in a transaction was performed for the loads given in the 24-bus IEEE RTS system for different seasons of the year, different weeks and different hours of the days. Based on the current and a few past operating scenarios Learning Coefficients were obtained and this enables the prediction of losses and contribution of generators for oncoming scenarios. The learning coefficients can be kept updated, as and when new scenarios come up. The results obtained showed that the predicted values are within acceptable limits. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/6083 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Power sharing, tracing and prediction of losses | en_US |
| dc.subject | deregulated operation of power systems | en_US |
| dc.title | Power Sharing, Tracing And Prediction Of Losses For Deregulated Operation Of Power Systems | en_US |
| dc.type | Thesis | en_US |
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