Fast Solution Method for the Large-Scale Unit Commitment Problem with Long-Term Storage

Long-term storage (LTS) can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy curtailment. However, long-term unit commitment (UC) with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals (state-of-charge (SOC) constraint of LTS, ramping rate, and minimum up/down time constraints of thermal units), resulting in a significant computational burden. Herein, an iterative-based fast solution method is proposed to solve the long-term UC with LTS. First, the UC with coupling constraints is split into several sub problems that can be solved in parallel. Second, the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling constraints. Third, a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the LTS. The price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub problem. Finally, the sub problem with the SOC boundary of the LTS is iteratively solved independently. The proposed method was verified using a modified IEEE 24-bus system. The results showed that the computation time of the unit combination problem can be reduced by 97.8%, with a relative error of 3.62%.