National Natural Science Foundation of China (NSFC) [61100159, 61233007]
; National High Technology Research and Development Program of China (863 Program) [2011AA040103]
; Foundation of Chinese Academy of Sciences [KGCX2-EW-104]
; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA06021100]
; Cross-disciplinary Collaborative Teams Program for Science, Technology and Innovation, of the Chinese Academy of Sciences Network
; system technologies for security monitoring and information interaction in smart grid, and energy management system for micro-smart grid
Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.