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In this paper, a stochastic electricity market model is applied to estimate the effects of significant wind power generation on system operation and on economic value of investments in compressed air energy storage (CAES). The model's principle is cost minimization by determining the system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics, and electricity demand. To obtain appropriate estimates, notably reduced efficiencies at part load, start-up costs, and reserve power requirements are taken into account. The latter are endogenously modeled by applying a probabilistic method. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of investments in selected thermal power plants and CAES. Results for a German case study indicate that CAES can be economic in the case of large-scale wind power deployment.Index Terms-Compressed air energy storage, power system modeling, stochastic systems, wind energy, wind integration costs.

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SUMMARYIn this paper a stochastic fundamental electricity market model is applied to estimate the integration costs of wind due to changed system operation and investments in Germany. The model's principle is cost minimization by determining the system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics, and electricity demand. To obtain appropriate estimates of the integration costs notably reduced efficiencies at part load and start-up costs are taken into account. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of reserve requirements and investments in thermal power plants. The results highlight the need for stochastic optimization models and the strong dependency on the actual system and its development over time to get sufficient estimates of the integration costs of wind's intermittency.

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