This study proposes an adaptive droop control strategy to be used in hybrid static synchronous compensator (STATCOM) systems. The proposed voltage-sensitivity-based adaptive droop control scheme using a hybrid STATCOM system is introduced to eliminate the possibility of interference with the other voltage regulation devices while achieving better voltage regulation without having to implement a full observation of the corresponding network. The sensitivity-analysis is performed by the voltage control of STATCOM system and the sliding-mode-control is included for control system robustness. A comparison between fixed and adaptive control methods is made through case studies of the IEEE 39-bus test system. The results were further verified through a transient simulation of the hybrid STATCOM system that considers both low and high penetration levels of inverter-based reactive power resources.

To achieve flexible operation of modern distribution networks with increasing renewable energy sources (RESs), this study proposes a stochastic operation method for a novel energy management platform that combines energy storage with a power flow controller, which is presented as a multi-terminal soft open point (MT-SOP). A Wasserstein metric-based distributionally robust chance-constrained optimization for the operation of the MT-SOP is proposed under the assumption that there is no prior knowledge about the distribution of forecast errors for both the load and RES outputs. The proposed method stochastically relaxes the reliability constraints on the bus voltage and branch power flow to maximize the cost-benefit of the distribution system operator (DSO). The proposed problem was reformulated into a tractable convex program using the concept of conditional value-at-risk. Based on case studies using modified IEEE-33 systems, the trade-offs between operational risks and cost–benefits were analyzed using parameters on the risk level and radius of the ambiguity set. Through numerical comparisons of the simulation results, we show that the DSO can achieve economic efficiency for imported electricity by relaxing conservativeness in distribution network reliability.

Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.

The proliferation of renewable energy resources (RES), especially solar photovoltaic (PV) generation resources, causes overvoltage and line overloading in distribution networks. This study proposes a two-level volt–var control method based on multiple timescales. The on-load tap changer (OLTC) operates on an hourly timescale, to regulate the voltage on the secondary winding. In the 15-minutes timescale, PV-connected smart inverters and static var compensators (SVCs) are obliged to compensate the reactive power for the voltage control at the point of common coupling. In the multi-timescale voltage control framework, this study proposes a new multi-sectional volt–var curve (MSVVC) of a PV inverter. The objective of the MSVVC is to minimize the energy loss in the network, improve the voltage profile, and obtain the operational margin of other reactive power compensation devices. In the process of determining the optimal parameters of the MSVVC, stochastic modeling-based load flow analysis is utilized to consider the intermittency and uncertainty of RES generation. The effectiveness of the proposed method is verified on the IEEE 33-bus system in comparison with the conventional volt–var curve cases.