Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

作     者：Fatouros, Panagiotis Konstantelos, Ioannis Papadaskalopoulos, Dimitrios Strbac, Goran 

作者机构：Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England 

出 版 物：《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》 (IEEE可持续能源汇刊)

年 卷 期：2019年第10卷第1期

页      面：459-469页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0808[工学-电气工程] 08[工学] 

基　　金：EPSRC [EP/N03466X/1  EP/K039326/1] Funding Source: UKRI 

主　　题：Aggregator distributed energy resources multi-dimensional uncertainty stochastic dual dynamic programming vector autoregressive modeling 

摘      要：The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.