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Chapter 6 market is presented; the CSP plant is run with a price-driven strategy and, based on electricity pric- ing and weather forecasting, an economically optimized bidding strategy for the day-ahead energy market is determined. The authors identify a period comprising the next one or two trading days as a reasonable optimization horizon, considering the trade-off between profit gain and forecast quality. More recently, the authors of Ref. [22] assessed the potential of a solar-thermal generation system in a fluctuating electricity prices context, by considering the innovative CSP technology proposed in Ref. [23]. Ref. [24] investigated the influence of the operation strategy, focusing on the charge/discharge process for a thermocline storage, on the yearly production of a parabolic trough CSP plant. The research work documented here stems from the need of generalizing the analysis on opti- mized dispatching strategies for CSP plants, by considering the whole design-space of present-days systems, e.g., in terms of storage capacity and solar multiple. Furthermore, a novel assessment of how different control procedures can influence the design of the plant and the financial perfor- mance of the project is presented. When a new CSP plant is being considered for construction at a specific location, models and tools are needed to assess the potential of eneregy production, and thus eventually compute the PPA price level that can repay for the investment within the specified time. A widely adopted, publicly available software tool for this purpose is the System Advisory Model (SAM) [10, 25, 26], which is assumed as a reference in this work. The TES control strategy assumed by SAM is such that, for each hourly interval of operation, the controller tries to use all the available thermal power from the solar field and from the TES to drive the power block at the max- imum possible load. This strategy is clearly sub-optimal when the TOD factor shows significant variations during peak hours, so it can be safely assumed that the plant being evaluated will eventu- ally be operated using some kind of optimal control that will try to maximize the economic revenue by exploiting the available storage instead of the strategy assumed by SAM. In order to make correct decisions in terms of plant sizing and design would be therefore advantageous to include optimal control even at this very early stage. To this end, the method presented here is based on a dynamic model of the plant, replicating the basic modelling assumptions of the SAM software, which is then employed to formulate and solve a dynamic optimization problem in order to give a credible estimation of the potential of a future CSP plant, assuming optimal control is used for its operation. Since the full details of the real- time implementation of the optimal controller cannot be available at this very early design stage, it is reasonable to consider an idealized set-up of the control problem, assuming perfect matching between the model and the plant dynamics, and perfect knowledge of the future solar irradiation. The attained performance represents therefore the theoretical limit of the operation of a real-time optimal controller, which in reality will have to face modelling errors, unmodelled disturbances, and uncertain weather forecasts. Although the obtained results will be slightly optimistic, they will represent controlled plant operation in a much more credible way than those obtained with the short-sighted control policy usually assumed. Modern object-oriented languages and simulation tools are used in order to concisely formulate and solve the optimal control problem with minimal implementation effort. The main goal of the work is to show that, by means of these techniques and tools, the note- worthy advantages offered by optimal operation procedures can be easily unveiled and taken into account during the earliest design stages. The chapter is structured as follows. The CSP plant model, replicating the main features of the SAM model, is introduced in §6.2. The reference control strat- egy and the optimal control problem are formulated in §6.3. The computational infrastructure is discussed in §6.4, while the main results are presented and discussed in §6.5. §6.6 illustrates the main conclusions and an outlook to future work. 152PDF Image | New Concepts FOR Organic Rankine Cycle Power Systems
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