In [18], four OPF-based short-term APC schemes are presented. The first scheme's objective represents PV harvesting (all PVs are curtailed equally), the second objective is related to energy export (all households are supplied equally), the third objective ensures the same financial benefit to all participants, and the last objective minimizes total APC (without considering fairness). [PERSON]'s fairness index quantifies and compares fairness in certain schemes. The linearized OPF model is tested on realistic 22 kV and low-voltage 4500 household distribution networks. The main question is which scheme represents the most fairness for all participants due to their dependence on multiple factors. The authors in [19] propose a decentralized voltage control strategy including APC fairness in ADN. A PV inverter, according to the obtained calculation of its node voltage and injected active power, shares information with other PV inverters, which adjust their active and reactive powers as control parameters. Like [18], [PERSON]'s fairness index is used to determine the fairness of PV inverters. Except for APC incentive schemes, ref. [20] deals with RPC incentive schemes. The main goal is to reduce power withdrawn from the external network adjusting PV inverter active and reactive power. As in [11], a multi-stage MISOCP optimization model is proposed for voltage violations in ADN mitigation. The first stage is related to the day-ahead dispatch of OLTC and CBs. The second stage differs from [11] in terms of presented two voltage control modes and a comparison of their benefits. Lastly, multi-dimensional local voltage control regulates active and reactive power output in real-time and corrects estimation from stage two. IEEE-33 bus test network and the CPLEX solution method are used. The concept of [22] is divided into two parts. The first part refers to ADN operation optimization with objectives of DG active and reactive control costs and network loss minimization. Decision variables include BESS, OLTC and active and reactive power from DG. In the second part, DN provides ancillary services such as voltage support to the transmission network. This optimization model is tested on a real-life Swiss balanced network. In conclusion, the authors emphasize that changing existing grid codes is necessary due to DG's reactive power potential. Unlike [10; 11], a distributed control scheme for adjusting PV inverter active and reactive power settings is proposed in [23; 24]. This control scheme uses PV inverter reactive power as a priority and, if necessary, includes PV active power. This scheme is compared with the OPF-based centralized control scheme that minimizes APC as the main objective. In this case, the OPF-based centralized control scheme represents the most expensive concept for developing, as well as more complex for operating. Another open question is related to the application of distributed control schemes on LV unbalanced distribution networks. As in [23], the authors in [25] propose a distributed control scheme for reducing APC and PV power loss by charging and discharging BESSs. PV inverters locally provide voltage support and reduce APC to a minimum with optimal allocation and coordination. In comparison to the previous works in which both PV active and reactive powers are used, [26] proposes a three-phase Volt-Var control method. The Volt-Var control approach is divided into three stages in [27] that include central and local Volt-Var control for voltage deviation and network loss reduction. The first two stages have a similar principle as in [13; 14]. Reactive power control in real-time is presented in the third stage. A fully linearized DistFlow is used to model the problem. The authors in [28] Volt-Var framework consists of optimal coordination of OLTC and CB day-ahead scheduling and intra-day dispatch of PV reactive power. The optimization problem is multi-objective and the authors propose four different algorithms to transform this problem into a single-objective and solve it.