Regression and Tracing Methodology based Prediction of Oncoming Demand and Losses in Deregulated Operation of Power Systems

The deregulated electricity market can be thought of as a conglomeration of generation providers, transmission service operators (TSO) and retailers, where both generation and retailing may have open access to the transmission grid for trading electricity. For a transaction contract bid to take shape, apart from the cost elements, inputs such as power required by a retailer and its corresponding required generation at the generation end, taking into account the expected overall power loss in the transaction, is essential. In a fully deregulated open access system, for framing of the transmission services hiring contract, inputs such as extent of use of a transmission circuit for a transaction and the associated power loss in the said transmission circuit are also required. To provide the necessary lead time to frame transaction and transmission contracts for an oncoming operational scenario, a capability to predict the stated inputs in advance, are desirable. Regression and Proportional sharing based power tracing method using linear equations, determines different transactions to supply a specific retailer’s demand and the losses related to each transaction. The learning coefficients are used advantageously to predict a generator’s contribution to a retailer’s demand and power loss for this transaction. This paper proposes a procedure that can be implemented real time, to quantify losses in each transmission circuit used by a specific transaction, based on proportionality between power flow and the associated loss, and then predict the same for an oncoming transaction.