Abstract. Reservoir operation plays an important role in economic development of a region. Hedging operations were used for municipal, industrial, and irrigation water supplies from reservoirs in the past. However, hedging operation for hydropower reservoir operation is very rare. A practically simple and useful new form of Standard Operation Policy and a new form of hedging rules for hydropower production are introduced in this paper and demonstrated with a case study for hydropower reservoir operation of Indirasagar reservoir system in India. The performance of optimal hedging rules is compared with that of a new standard operation policies and the superiority (reliability increases by about 10%) of the hedging rules is demonstrated. When the number of decision variables is increased from 5 to 15, energy production increases by 0.7%, the spill is reduced by 16.8%, and reliability slightly decreases by 2.1%. A bi-level simulation-optimization algorithm is used for optimizing the hedging rules. For optimization, Genetic Algorithm, arti cial bee colony algorithm, and imperialistic competitive algorithms are utilized. The results indicate that all the three algorithms are competitive and arti cial bee colony algorithm is marginally better than the other two.

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The application of hedging rules in reservoir operation has been established as one of the important advances in the field of reservoir operation studies during the past three decades. Hedging rules distribute the deficits over a longer duration to minimize the impact of deficits. Thus, hedging provides the insurance of highvalued water uses, where reservoirs experience uncertain inflows. Formulating different forms of hedging rules and proposing appropriate objective functions that adequately describe the trade-off among the operational performance indicators have been attempted for water supply and irrigation release from reservoirs. Research on hedging rule-based operation of reservoirs for hydro-power generation has not yet gained sufficient attention, probably due to its complexity due to non-linearity. In this study, hedging rules are formulated for Indira Sagar reservoir for hydro-power generation. Discrete phased hedging rule and twopoint linear hedging rule are developed and demonstrated in this research. While the two-point linear hedging rule is one of the simple hedging rules, the discrete phased hedging rule is a more realistic rule as it will facilitate better planning of alternative sources of power generation or rationing. The results indicate the advantages of applying the hedging rules over the standard operating policy.

Preserving the privacy of biometric data becomes a critical work. To increase the privacy of the biometric data, novel method is proposed. In this proposed method, two different biometric data such as features from fingerprint and face are combined. In the face, the features like eyes, lips and brow are extracted. In the fingerprint, orientation feature is extracted. The database contains the training images. The ELM classifier is used to combine these features and matches with the training image.