考虑调度决策非预期性的多阶段随机规划调度策略

随着可再生能源渗透率的提高,分布式可再生能源带来的波动性、间歇性会传递至主网中,对系统安全运行造成影响,研究不确定性优化方法对系统实际运行具有一定的指导作用。传统的随机优化以及鲁棒优化方法不满足系统实际运行的非预期性要求。文章以日运行期望成本最小为目标,考虑分布式可再生能源发电不确定性,建立多阶段随机规划模型,可以根据之前不确定信息的实现在每个阶段确定预调度决策,不会受到未来不确定信息的影响,符合系统实际运行规律,满足非预期性。为了避免多阶段随机规划问题求解的维数灾难,采用随机对偶动态规划(stochastic dual dynamic programming, SDDP)算法进行求解。仿真结果表明,相比于传统的确定性模型,多阶段随机优化得到的最优调度决策树较之确定性优化得到的单一决策方案具有更广泛的决策空间,可以基于上一阶段不确定信息的实现和决策来更新调度决策,降低系统的运行成本。

Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision

With the increase of renewable energy penetration, the volatility and intermittence brought by distributed renewable energy will be transferred to the main network, which will affect the safe operation of the system. The study of uncertainty optimization method plays a certain guiding role in the actual operation of the system. However, the traditional stochastic optimization and robust optimization methods do not meet the unpredictable requirements of the actual operation of the system. In this paper, a multi-stage stochastic programming model considering the uncertainty of distributed renewable power generation is established with the goal of minimizing the expected cost of daily operation. The pre-scheduling decision can be made at each stage according to the realization of the previous uncertain information, which will not be affected by the future uncertain information, and is in line with the actual operation law of the system, and meets the unexpected requirements. In order to avoid the disaster of dimensionality in solving multi-stage stochastic programming problems, the stochastic dual dynamic programming algorithm is used to solve the problem. The simulation results show that, compared with the traditional deterministic model, the optimal scheduling decision tree obtained by multi-stage stochastic optimization has wider decision space than the single decision scheme obtained by deterministic optimization. The scheduling decision can be updated according to the implementation and decision of the uncertain information in the previous stage, and the operating cost of the system can be reduced.