A multi-time scale co-optimization method for sizing of energy storage and fast-ramping generation

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Power systems need to have sufficient generation capacity to support the demand at all times. In addition, dispatchable generation resources should be capable of adjusting their power output on a short term basis not only to alleviate uncertainties of nondispatchable generation and load fluctuations but also to correct for forecast errors. This paper presents a long-term planning approach to co-optimize capacities of energy storages and fast-ramping generation. We model and integrate the capability of the storage to provide multiple services for the system. Our formulation takes into account wind generation and demand forecast errors as well as short-term fluctuations. A stochastic optimization problem is formulated consisting of hourly and intrahour time scales. The approach determines the optimal size of newly deployed generation and storage resources to provide adequate generation capacity and ramping needed to support hourly demand. Additionally, our method ensures that the system is capable of following the net load in intrahour time intervals, as well as mitigating the impact of short-term wind power and load fluctuations. In this formulation, power balance, network security, and system ramping capability are stochastic constraints being modeled as chance constraints. A 3-bus and the IEEE 24-bus test systems are studied to show the effectiveness of the proposed approach.

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IEEE Transactions on Sustainable Energy

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