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.
Highlights
There is a strong demand for increasing the penetration level of wind energy into distribution networks (DNs)
A novel multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework was introduced to deal with fast-changing wind power in the presence of battery storage systems (BSSs)
The framework consists of three phases: In the first phase, a dynamic mixed-integer nonlinear programming problem is solved to simultaneously determine the optimal operation strategies of the BSSs for the upcoming day
Summary
There is a strong demand for increasing the penetration level of wind energy into distribution networks (DNs). Flexible operation of BSSs and considering both DoD and the number of charge-discharge cycles in the calculation of expended life costs of batteries lead to more efficient operation of BSSs but highly complex dynamic MINLP AR-OPF, in particular when all mixed-integer decision variables are simultaneously optimized. AR-OPF framework is developed to optimally react to the spontaneous changes in wind power and ensure the feasibility of operations in real time when BSSs exist in DNs. The framework offers the possibility of simultaneous optimization of all of the following mixed-integer variables in a prediction horizon:. Flexible optimal operation strategies for BSSs are determined for the dynamic AR-OPF while minimizing the expended life costs of the BSSs as a function of DoD and the number of charge-discharge cycles. The results of a case study and conclusions are provided in Sections 5 and 6, respectively
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