The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric operation of smart inverters (S-INVs) is required to cost-effectively achieve the same level of power system operational performance as before under circumstances where the spatio-temporal behavior of power flow is becoming significantly complex due to the penetration of DERs. This review provides an overview of current ambitious efforts toward smartization of operational management of DER inverters, clarifies the expected contribution of machine learning technology to the smart operation of DER inverters, and attempts to identify the issues currently open and areas where research is expected to be promoted in the future.

Grid congestion is expected owing to the interconnection of massive renewable energies within the transmission system. Power system operators need to resolve the problem by controlling the output of thermal and renewable generators, such as curtailment, which should be the smallest possible for efficient utilization. Moreover, the equality of generation opportunities should be maintained among renewable energies. However, achieving the two objectives simultaneously is challenging, because the output curtailment effect on grid congestion in a loop system varies depending on its configuration. Further, the thermal generators are curtailed before renewable energies in the merit order scheme necessitating adjustments in the thermal generator output considering the renewable energy curtailment. This study proposed a method to determine the output curtailment ratio of thermal power and renewable energies employing sensitivity analysis, aiming to ensure equality, and maximize the power generation opportunities of renewable energies for the mitigation of grid congestion. The effectiveness of the proposed method was verified via numerical simulations using a power system model based on an actual power system in the northern Tohoku area of Japan. The results indicate that the proposed method can reduce the total amount of curtailment and improve the equality of generation opportunities. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

To utilize renewable energy systems (RES) as main generators in power system, power system operators need to maintain reserve power to compensate the uncertain output of RES, such as wind farms (WF). This paper focuses on the balancing group (BG) composed of WF and a pumped-storage hydro generator (PSHG). In the BG operation, power system operators need to schedule the operation of PSHG to minimize the imbalance risk caused by the prediction error of WF output while considering the system specific constraints of PSHG, e.g. the mechanical constraint on pump-up/down switching. This paper proposes a method to schedule the PSHG operation using the interval prediction results of the WF output for alleviating the imbalance risk the proposed scheduling scheme is formulated based on the linear programming. To verify the effectiveness of the proposed method, the amount of imbalance and the net generation of the BG were evaluated based on numerical experiments under two types of situations with different characteristics in WF output. The experimental results reveal the effectiveness of the proposed method.

Transmission system operators (TSO) are responsible to manage the power quality within a proper range and to reduce power loss. Voltage reactive power control (VQC) is mainly utilized to manage the power flow. In the conventional VQC scheme, the static condenser (SC) and shunt reactor (ShR) is generally adjusted based on the target value determined for the heavy load condition in each voltage class. On the other hand, the installation of the massive photovoltaic (PV) systems into transmission systems cause the complex power flow such as the short-term fluctuation of power flow and reverse power flow from the lower transmission system to the upper transmission system. Thus, in the future power system with PV systems, the target values of the VQC devices need to be updated in the short-time period considering the interaction between the upper and lower transmission system to manage the power quality and to the reduce power loss efficiently. This paper proposes the optimization methodology for VQC devices in two-voltage class of transmission system using tabu-search and optimal power flow based on the central VQC methodology. To verify the effectiveness of the proposed methodology, the numerical simulation was performed based on the revised IEEJ EAST 30-machine system model.

This paper proposes a method to determine the Line Drop Compensator (LDC)-control parameters of an On-load Tap Changer (OLTC) and step voltage regulator (SVR) using a database with measured data from offline IT-switches. In a conventional determination method, the parameters are calculated based on the relationship between the sending voltage and the absolute value of current at the secondary side of the OLTC/SVR with a fixed power factor. However, the power factor changes with the interconnection of the photovoltaic (PV) system. Hence, in the proposed method, LDC-control parameters are determined by the relationship between active current, reactive current, and sending voltage. In other words, the parameters are determined from the plane by taking these values as axes. The plane is determined by four measured data extracted from the database which have large voltage fluctuations in response to severe power flow. The effectiveness of the proposed method is verified by numerical simulations using a distribution system model with PV systems. Our results show that the proposed method can improve the amount of voltage violation by 35% compared to a conventional method.