An efficient two stage stochastic optimal energy and reserve management in a microgrid. (15th December 2015)

• Record Type: Journal Article
• Authors: Mohan, Vivek; Singh, Jai Govind; Ongsakul, Weerakorn
• Is Part Of: Applied energy. Volume 160(2015:Dec. 15)
• Page Start: 28
• Page End: 38
• Publication Date: 2015-12-15
• Subjects: Stochastic -- Microgrid -- Energy management -- Affine arithmetic -- Spinning reserve; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.09.039
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A two stage stochastic optimal energy management for a microgrid is proposed. It can consider all possible sources and levels of nodal power uncertainties. In first stage, energy and reserve dispatch for possible uncertainties are estimated. Second stage fetches and sends dispatch set points directly from the database. The estimated bounds for the dispatches are more precise and cost effective. Abstract: In this paper, an efficient two stage stochastic optimal energy and reserve management approach is proposed for a microgrid. In the first stage, the optimal power schedule is determined based on the load, wind and solar power forecasts. The possible uncertainties in forecasts are expressed as perturbations in nodal power injections and the corresponding optimal spinning reserves are estimated using sensitivity analysis. Using this information system, the actual spinning reserve for the discrepancy between the measured and forecasted data is directly dispatched at stage-2, utilizing the remaining capacity of demand response, grid purchase and other non-renewable distributed energy resources (DERs). A stochastic perturbed optimal power flow (OPF) based on affine arithmetic (AA) and stochastic weight tradeoff particle swarm optimization (SWT-PSO) is proposed and investigated on CIGRE LV benchmark microgrid. The approach is found to be better in terms of operational planning, real time computation and bounds of power flow & cost variables.