Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

Erfan Mohagheghi, Mansour Alramlawi, Aouss Gabash, Frede Blaabjerg and Pu Li
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Erfan Mohagheghi: Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany
Mansour Alramlawi: Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany
Aouss Gabash: Department of Automation Engineering, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany
Frede Blaabjerg: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Pu Li: Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany

Energies, 2020, vol. 13, issue 7, 1-17

Abstract: In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

Keywords: real-time dynamic active-reactive optimal power flow (RT-DAR-OPF); feasibility; MINLP; battery storage systems (BSSs); intermittent wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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