The interest in installing PV-based microgrids has increased significantly in the last years due to the urgent need for reducing greenhouse gas emissions and improving the reliability as well as the quality of power supply, in particular in developing countries. The design of a microgrid is challenging since multiple components and complicated operating conditions have to be taken into account. In this paper, a comprehensive method for optimal design of a class of residential PV-battery microgrids is proposed to determine the optimal number of lead-acid batteries and PV-panels, the optimal battery bank depth of discharge (DOD) value, and the optimal tilt angle of the PV-panels. The aim of the optimization is to minimize the levelized cost of energy (LCOE) where the limitation of the annual total loss of the power supply (TLPS) and the operational constraints are considered. In addition, a detailed model for battery lifetime estimation is introduced based on the physico-chemical mechanism of the lead-acid battery. Moreover, the effect of design parameters, e.g., the size and DOD of the battery bank on the battery capacity degradation, the PV-Panel number and battery lifetime on LCOE, is investigated. Computation results demonstrate that the proposed method is capable of optimizing the size of the microgrid components and the other design parameters while satisfying all technical and operational constraints.

There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.

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.