Data-Driven Distributionally Robust Energy-Reserve-Storage Dispatch
Data-Driven Distributionally Robust Energy-Reserve-Storage Dispatch
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DOI：
10.1109/tii.2017.2771355
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发表时间：
2018-07
影响因子：
12.3
通讯作者：
C. Duan;Lin Jiang;W. Fang;J. Liu;Shiming Liu
中科院分区：
文献类型：
--
作者：
C. Duan;Lin Jiang;W. Fang;J. Liu;Shiming Liu
为了便于多变、不确定的可再生能源的集成，提出了分布式稳健的能量-储备-存储联合调度模型和方法。通过模糊集来刻画可再生能源预测误差的不确定性，模糊集是一组与观测历史数据一致的概率分布。该模型以模糊集合中最坏情况分布对应的期望运营成本最小为目标。采用分布稳健的机会约束来保证备用和传输充分性。历史数据越多，歧义集越小，解的保守性越小。该公式最终转化为一个混合整数线性规划，其规模随着历史数据的增加而保持不变。引入了非主动约束识别和凸松弛技术，减少了计算负担。数值结果和IEEE118节点系统的蒙特卡罗仿真结果表明了该方法的有效性和高效性。
This paper proposes distributionally robust energy-reserve-storage co-dispatch model and method to facilitate the integration of variable and uncertain renewable energy. The uncertainties of renewable generation forecasting errors are characterized through an ambiguity set, which is a set of probability distributions consistent with observed historical data. The proposed model minimizes the expected operation costs corresponding to the worst case distribution in the ambiguity set. Distributionally robust chance constraints are employed to guarantee reserve and transmission adequacy. The more historical data are available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the number of historical data increases. Inactive constraint identification and convex relaxation techniques are introduced to reduce the computational burden. Numerical results and Monte Carlo simulations on IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.