This paper presents a linear mixed integer modeling approach for basic components in a biomass supply chain including supply, processing, storage and demand of different types of biomass.The main focus in the biomass models lies on the representation of the relationship between moisture and energy content in a discretized framework and on handling of long term processes like storage with passive drying effects in the optimization. The biomass models are formulated consistently with current models for gas, electricity and heat infrastructures in the optimization model 'eTransport', which is designed for planning of energy systems with multiple energy carriers. To keep track of the varying moisture content in the models and its impact on other biomass properties, the current node structure in eTransport has been expanded with a special set of biomass nodes. The Node, Supply, Dryer and Storage models are presented in detail as examples of the approach. A sample case study is included to illustrate the functionality implemented in the models.

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In deregulated electricity markets, hydropower producers must bid their production into the day-ahead market. For price-taking producers, it is optimal to offer energy according to marginal costs, which for hydropower are determined by the opportunity cost of using water that could have been stored for future production. At the time of bidding, uncertainty of future prices and inflows may affect the opportunity costs and thus also the optimal bids. This paper presents a model for hydropower bidding where the bids are based on optimal production schedules from a stochastic model. We also present a heuristic algorithm for reducing the bid matrix into the size required by the market operator. Results for the optimized bids and the reduction algorithm are analyzed in a case study showing how uncertain inflows may affect the bids.

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