在“双碳”目标的背景下,大规模风光资源的接入和消纳是日后能源发展的必然趋势,但随着风光并网容量的增长,电力系统也需要更多的灵活性资源来保障安全运行.水电是一种可再生的灵活性资源,具有优异的灵活调节能力.为研究水电在系统中的灵活调节作用,以雅砻江流域水风光蓄清洁能源基地下游部分电站为研究对象,考虑灵活调节能力,开展互补系统的日前优化运行策略研究.首先,为解决独立抽蓄的选址难和造价高问题,建立考虑梯级水电功能再造混合式抽蓄电站的梯级水风光蓄模型.针对传统风光模型预测精度低和主观选取长短期记忆(LSTM)网络超参数的局限性,采用粒子群算法(PSO)优化LSTM模型预测风光出力.随后,为充分挖掘互补系统的灵活调节潜力,构建了考虑互补系统经济效益和灵活调节裕度的日前多目标优化调度模型.采用法线边界交叉(NBI)法对构建的多目标问题进行求解,可获得分布均匀的Pareto最优解.最后,基于雅砻江流域实际情况进行算例分析,并通过不同场景分析验证了本文模型的有效性和抽蓄对系统灵活性运行的支撑作用,结果表明所提方法在兼顾系统收益的同时能充分挖掘系统的灵活调节潜力,保障系统的稳定运行.

In the context of “carbon peaking and carbon neutrality”, the large-scale integration and consumption of wind and solar resources is an inevitable trend in future energy development. However, as the capacity of wind and solar power integration increases, the power system also requires more flexible resources to ensure secure operation. To investigate the flexible regulation of hydropower in the system, this study focuses on the downstream stations of the hydro-wind-solar-pumped storage clean energy base in the Yalong River Basin. Considering its flexible regulation capabilities, the study conducts day-ahead optimized operational strategy research for the complementary system. First, to address the challenges of site selection and high costs associated with independent pumped storage, steady-state models for hybrid pumped storage stations in a cascade hydro-wind-solar-pumped storage system are established. To overcome the limitations of traditional models such as low predictive accuracy and the subjective selection of long short-term memory (LSTM) hyperparameters, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of LSTM and the optimized LSTM model is then used to forecast the output of wind and solar power. Next, in order to fully harness the flexible regulation potential of the complementary system, a multi-objective optimal dispatching model is developed considering the economic benefits and flexible regulation margin of the complementary system in the day-ahead time. The normal boundary intersection (NBI) method is employed to solve the multi-objective problem, which can obtain the Pareto optimal solutions with an even distribution. Finally, case studies are conducted based on the actual conditions of the Yalong River Basin. By analyzing different scenarios, the effectiveness of the proposed model and the supportive role of pumped storage in enhancing system flexibility are validated. The results demonstrate that the proposed approach not only balances system profits but also fully exploits the flexible regulation potential of the system, ensuring stable operation of the system.