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Abstract-This paper presents a medium-term self-scheduling optimization of pumped hydro storage power plants with detailed consideration of short-term flexibility. A decomposition of the problem into inter-and intrastage subproblems, where the intrastage problems themselves are formulated as multi-stage stochastic programs, allows the detailed consideration of shortterm flexibility. The method is presented together with three alternative approaches, where the short-term flexibility is considered differently: (1) with aggregated peak and off-peak prices, (2) with price duration curves and (3) with deterministic intrastage subproblems. The methods are compared and evaluated in a Monte Carlo operation simulation study. The study is performed on a realistic hydro power plant with consideration of revenue from ancillary services.

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This paper analyzes the management of a large number of distributed battery energy storage systems (BESSs) by a energy utility in order to provide some market services. A heuristic algorithm based on two parts is proposed for this task. The first part, the aggregation, combines the abilities and behavior of the fleet of BESS into a virtual power plant (VPP) by a concise but flexible model. This VPP can be used by the utility as they are used to with traditional power plants. The second part, the disaggregation, distributes VPP control schedules back to the individual BESS by a greedy first-fit decreasing heuristic. The management of a fleet of BESS can also be modeled as a mathematical linear optimization program. The proposed heuristic is compared to and evaluated against this global optimization regarding computational performance and quality of results. It is shown, that the heuristic provides a remarkable speedup when applied to larger number of units. With it, it is possible to handle a group of at least 100,000 individual BESS. Further, the quality of the results are shown. First, the solution of the heuristic is compared to the optimal one of the mathematical program. Second, the methods are both applied and compared in a realistic case study.