Abstract:

High temperature gas-cooled reactor pebble-bed module demonstration power plant (HTR-PM) is designed to operate over wide conditions to cope with the ever changing power demand. Model-based operation optimization is challenged by the inability to obtain models that are well-suited for significant loads changes. To deal with plant-model mismatch, an adaptive MA (modifier adaptation) algorithm is proposed for significant load changes in HTR-PM. This algorithm corrects the optimization model by process feedback information, which promotes the consistency between the optimization model and the plant optimization problem, and thus drives model-based operation optimization to converge to the true optimum. Within the framework of trust region, the adaptive MAs algorithm adaptively updates the model and determines the application range of the optimization model based on model evaluation, so as to confine the optimization model to an appropriate operating region. Moreover, the trust region framework reduces the sensitivity of algorithm performance to algorithm parameters. The proposed algorithm is applied to significant load changes in HTR-PM where both reactors undergo load changes synchronously, and the results show the efficiency of this method.

Key words: model, optimization, algorithm, significant load changes, HTR-PM

摘要：

为应对电网负荷需求的变化，球床模块式高温气冷堆核电站示范工程(HTR-PM)在设计上具备大范围变负荷运行以满足电网负荷的能力。由于无法获得大范围变负荷精确适用的模型，使得基于模型的操作优化面临挑战。为处理模型与对象的失配，提出HTR-PM大范围变负荷的MA(modifier adaptation)自适应优化算法。MA自适应优化算法利用过程反馈信息修正优化模型，促进优化模型与对象优化命题的一致性，进而有助于基于模型的操作优化收敛至对象的最优操作。借助信赖域框架，MA自适应优化算法可基于模型评价自适应更新模型、调整修正的优化模型的应用范围，确保在合适操作空间内求解优化模型。而且，信赖域框架还降低了算法性能对算法参数的敏感性。MA自适应优化算法在HTR-PM双堆同步大范围变负荷中的应用验证了方法的有效性。

关键词: 模型, 优化, 算法, 大范围变负荷, HTR-PM