Abstract-In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through auctions in a daily market. Developing effective strategies to optimize hourly offer curves for a hydrothermal power system to maximize profits has been one of the most challenging and important tasks for a GenCo. This paper presents an integrated bidding and scheduling algorithm with risk management under a deregulated market. A stochastic mixed-integer optimization formulation having a separable structure with respect to individual units is first established. A method combining Lagrangian relaxation and stochastic dynamic programming is then presented to select hourly offer curves for both energy and reserve markets. In view that pumped-storage units provide significant energy and reserve at generating and pumping, the offering strategies are specially highlighted in this paper. Numerical testing based on an 11-unit system with a major pumped-storage unit in the New England market shows that the algorithm is computationally efficient, and effective energy and reserve offer curves are obtained in 4-5 min on a 600-MHz Pentium III PC. The risk management method significantly reduces profit variances and, thus, bidding risks.

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Soil organic carbon (SOC) is frequently determined by the Walkley‐Black (WB) method. A limitation of the test is incomplete oxidation of the carbon fraction and underestimation of SOC. Automated dry combustion methods are expensive and slow. Optical sensing and chemometric analysis offer the potential of an economical method capable of quantifying SOC fractions. The aim of this study was to identify the best SOC analysis method to facilitate maximum sampling resolution based on the cost per sample, analytical accuracy and time. A comparative evaluation was made of five techniques; (1) the WB method, (2) total combustion by total organic C analyser, (3) infrared (IR) diffuse reflectance spectroscopy, (4) a portable spectroradiometer and (5) laboratory hyperspectral imaging. This involved assessing equipment costs, consumables and time to derive total analytical cost. The benefits were sample throughput and analytical accuracy. Instrumentation represented the largest input to analytical cost and for optical methods was governed by the spectral range. In contrast to dry combustion, this cost is offset by high sample throughput and minimal consumable requirements for IR spectroscopy and hyperspectral imaging. Hyperspectral imaging is identified as the most rapid technique with potential to scan about 720 samples per day at 90% less cost than the WB method. The opportunity cost of hyperspectral imaging is to forfeit some analytical accuracy associated with the dry combustion method. Dry combustion, despite its high cost per sample, incurs no further costs associated with updating prediction models or developing site or soil specific correction factors.

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