Co-planning of transmission and energy storage by iteratively including extreme periods in time-series aggregation

Abstract

The co-planning problem of transmission and energy storage system (ESS) requires a large amount of historical and forecasted input data to account for the volatility of renewable energy and loads. However, the large input data usually make the planning problem difficult to solve, so time series aggregation is often used to reduce the computational complexity. Nevertheless, it is difficult to guarantee the reliability of operation on the whole input data. Therefore, this paper proposes an iterative method to select extreme scenarios, and designs two indicators to select extreme scenarios, considering the system power balance and peak shaving capacity situation. Based on these two indicators, the periods of maximum load shedding and the periods of maximum renewable energy curtailment will be selected as extreme scenarios in the results of the operational optimization problem. We iteratively add extreme scenarios to the set of scenarios of the planning problem until the reliability of system operation can be adequately met. At the same time, in order to ensure the effectiveness of extreme scenarios, the operation statuses of thermal units in those periods are also taken into account. Our method is tested on an IEEE RTS-24 system with some modification. The results show that our method can guarantee the reliability of the whole system and is superior to the method that simply selects extreme scenarios. Meanwhile, we also perform a sensitivity analysis of the price of energy storage.