We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a robust forecast. We define a robust forecaster as one which has consistency in forecast accuracy, lesser shocks (outliers) and lower standard deviation in the distribution of forecast errors. We propose a composite ranking (CRank) scheme based on a composite score which considers three performance measures—standard deviation, kurtosis of distribution of forecast errors and accuracy of forecasts. The CRank helps in identification of a robust forecasts given a choice of individual and combined forecaster. The empirical analysis has been done with the real life data sets of two distribution companies in India.

We consider the problem of fair allocation of the cost of a transmission system among load and generation entities using the marginal participation approach. We show that a cost-causal approach involving capacity-based line cost rate and a min-max fair economic slack bus selection for price-taking entities leads to arigorously fair and more accurate implementation of marginal participation method. In the existing methods the counter-flows are masked, which is a compromise with fairness and linearity. However, if the counter-flows areincentivized then it can lead to pay-offs to some entities. The proposed approach solves the problem of pay-offs without masking the counter-flows. This is achieved by separation of the total transmission services cost into usage, reliability and residual capacity components. The allocation of the first two components is based on the min-max fairness policy, and the residual capacity costs are allocated on a pro-rata basis. Simulation results on multiple IEEE test systems, Indian utility power systems and extensive comparative evaluations for the contemporary methods demonstrate the claims made.