This paper proposes a method to determine an optimal radial-loop configuration to minimize power loss in a distribution network with photovoltaic (PV) systems and evaluates the effectiveness of this configuration. Due to the disaggregation of transmission and distribution in a deregulated power system, stable and efficient operations in terms of voltage regulation and power loss reduction utilizing existing equipment is becoming more important for distribution system operators (DSOs). Japanese DSOs operate distribution networks in a radial configuration while supplying power to customers with high reliability. One method to reduce further power loss is to upgrade the network topology to a radial-loop configuration, which achieves partial loop structures by closing the tie-switches of the radial configuration. To implement a radial-loop configuration, DSOs must first evaluate the reliability, as a loop configuration can cause a feeder circuit breaker (FCB) malfunction and an expansion of the nonsupplied area during fault conditions. However, the impact of a radial-loop configuration on reliability and effectiveness has not been verified. Therefore, this paper proposes a method to determine an optimal radial-loop configuration that minimizes active power loss and maintains high reliability. In a numerical simulation, the reliability of radial-loop configurations is verified, and the effectiveness of the configuration is analyzed in terms of active power loss and voltage regulation under several PV system penetration conditions. A 6.6 kV distribution network consisting of 6 feeders and 11 tie-switches is modeled based on an existing Japanese distribution network and used for numerical simulation.

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.

1. The introduction of renewable energies is accelerating in numerous countries to achieve the greenhouse gas emission reduction targets of the Paris Agreement for climate change. In particular, wi...

Virtual synchronous generators (VSGs) have been developed to mitigate the increase in the rate of change of frequency (ROCOF) in power systems by replacing synchronous generators (SGs) with inverter-based resources. VSGs mimic the dynamics and control of SGs; however, the mechanical delay typical of an SG’s turbine is often excluded, limiting improvements to the VSG’s response. The fast frequency response (FFR) of VSGs can help reduce ROCOF and enhance emulated inertia. This implies that the effective inertia (EI) of VSGs can exceed the set inertia parameter, posing challenges for operators in allocating sufficient VSG capacity considering the inertia required for stable operation. In this study, we quantitatively analyzed the difference between the EI of a VSG and the set inertia parameter by separating the active power output into inertia and governor responses. The quantitative analysis revealed that when the VSG provides FFR within the inertia-time domain, the EI exceeds the set inertia parameter. Furthermore, the sensitivities of the VSG set parameters and VSG capacity ratio, which is related to synchronizing power coefficients and the initial sharing ratio, for the EI were analyzed. These factors were theoretically considered, and the simulations validated their characteristics.

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved.

This study proposes a novel output curtailment method of renewable energy sources (RESs) to mitigate grid congestion while balancing curtailment minimization and fairness among power producers, considering the operational efficiency of system operators. This method is based on creating zones using sensitivity analysis and clustering grid congestion conditions. Recently, the large introduction of RESs has raised concerns of grid congestion in power systems. To mitigate congestion, the system operator curtails the output of RESs; however, the curtailment should be minimized while ensuring equal generation opportunities among power producers. The proposed method divides RESs into multiple zones according to the congestion mitigation effect of their output curtailment and uniformly curtails them in each zone. Moreover, the method divides congestion conditions throughout the year by clustering and creates zones of RESs for each congestion pattern. This approach aims to apply the optimal zone division of RESs according to the congestion condition, while removing the burden on the operator to recreate the zone division for each time step. A simulation was conducted to evaluate the effectiveness of the proposed method using a bulk power system model in the northern area of Tohoku region of Japan. Based on the simulation results, our proposed method outperforms existing approaches in terms of minimizing RES curtailment and promoting fairness across power producers, while considering the frequency of zonal creation.