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Solar panel installations have increased significantly in Japan in recent decades. Due to this, world trends, such as clean/renewable energy, are being implemented in power systems all across Japan—particularly installations of photovoltaic (PV) panels in general households. In this work, solar power was estimated using solar radiation data from geographic information system (GIS) technology. The solar power estimation was applied to the actual distribution system model of the Jono area in Kitakyushu city, Japan. In this work, real power consumption data was applied to a real world distribution system model. We studied the impact of high installation rates of solar panels in Japanese residential areas. Additionally, we considered the voltage fluctuations in the distribution system model by assessing the impact of cloud shadows using a novel cloud movement simulation algorithm that uses real world GIS data. The simulation results revealed that the shadow from the cloud movement process directly impacted the solar power generation in residential areas, which caused voltage fluctuations of the overall distribution system. Thus, we advocate distribution system planning with a large number of solar panels.

An analytical method for placement and sizing of distributed generation on power distribution systems for loss reduction is introduced. The proposed analytical method is developed based on a new formulation for the power flow problem, which is non-iterative, direct, and involves no convergence issues even for systems with high R/X branch ratios. Further, this power flow solution is extremely useful whenever fast and repetitive power flow estimations are required. A priority list based on loss sensitivity factors is developed to determine the optimal locations of the candidate distributed generation units. Sensitivity analysis is performed to estimate the optimal size and power factor of the candidate distributed generation units. Various types of distributed generators (DGs) have been dealt with and viable solutions are proposed to reduce total system loss. The proposed method has been tested on 33-bus and 69-bus distribution systems, which are extensively used as examples in solving the placement and sizing problem of DGs. Exhaustive power flow routines are also performed to verify the sizes obtained by the analytical method. The test results show that the proposed analytical method could lead to optimal or near-optimal solution, while requiring lower computational effort.

This study investigates the impacts of stealthy false data injection (FDI) attacks that corrupt the state estimation operation of power distribution systems (PDS). In particular, the authors analyse FDI attacks that target the integrity of distribution systems optimal power flow (DSOPF) in order to maximise the system operator losses. The branch current state estimation method is implemented to accurately model the PDS, and convex relaxations are applied to the DSOPF model. The effects of the FDI attacks are analysed on the IEEE 34‐bus unbalanced radial distribution system, with distributed energy resources (DERs) along the feeder. A 24 h DSPOF is performed, and the results depict the changes in the voltage profile and the additional power injection from the DERs, which consequently lead to the increase of the DSOPF cost.

Power loss reduction can be considered as one of the main purposes for distribution system operators. Reconfiguration is an operation process used for this optimisation by means of changing the status of switches in a distribution network. Recently, all system operators tried their best in order to obtain well-balanced distribution systems to decrease the operation cost, improve reliability and reduce power loss. This study presents an efficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conventional distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements, because it only considers loss and voltage deviation as objective functions. The proposed approach considers reliability, operation cost and loss simultaneously. By adding the reliability objective to the DFR problem, this problem becomes more complicated than before and it needs to be solved with an accurate algorithm. Therefore this study utilises an Enhanced Gravitational Search Algorithm called EGSA which profits from a special mutation strategy in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The proposed approach has been applied to two distribution test systems including IEEE 33 and 70-node test systems.

This paper offers an enhanced voltage stability assessment index (VSAI) and loss minimalize condition (LMC) centered integrated planning approach. The proposed method aims at the simultaneous attainment of voltage stability, loss minimizations and various other related objectives with the employment of multiple distributed generation (DG) units, in mesh distribution systems (MDS). The approach presents two enhanced VSAI expressions based on a multiple-loops configured equivalent MDS model. The main objective of each VSAI expression is to find the weakest buses as potential candidates for single and multiple DG placements with initial optimal DG sizes for aimed objectives attainment in MDS. Later, mathematical expressions for LMC have been presented, based on equivalent MDS model. The LMC aims to achieve significant loss minimization with optimal DG sizes and attain negligible voltage difference across tie-line branches via reduction of respective loop currents. The proposed integrated VSAI-LMC based planning approach is employed with two computation variants and tested on two well-known, 33-Bus and 69-Bus, test distribution systems (TDS). The performance analysis of each TDS is conducted with two cases and respective scenarios, across various performance evaluation indicators (PEIs). The paper also offers a comparative analysis of achieved numerical outcomes of the proposed planning approach with the available research works found in the literature. The numerical results attained have better performance in comparison with the presented literature data and thus shows the effectiveness and validity of the proposed planning approach.

Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability. Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses. In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages. This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events. In the first level, optimal islands are selected according to the graph model of the distribution system. In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management. The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods. The OPF problem in the second level is solved in DIgSILENT software. The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm.

With the aging of electricity transmission and distribution infrastructures and increasing intensity of extreme weather events, the aggravated vulnerability of electric distribution systems to extreme weather events has motivated the study of resilient distribution systems. This study presents a review of the state‐of‐the‐art research on distribution grid resilience. First, the definition and quantifying metrics of resilience in the electrical distribution system are summarised. Second, the long‐term and short‐term measures to enhance the distribution system resilience are discussed. In particular, the recent studies on distributed generation and microgrid‐assisted resilience enhancements are reviewed. Finally, recommendations for future research are presented.

Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to its top‐down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug‐in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom‐up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100‐feeder test system demonstrate the efficiency of MILP‐based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.

Purpose of Review Increased charging needs from widespread adoption of battery electric vehicles (EVs) will impact electricity demand. This will likely require a combination of potentially costly distribution infrastructure upgrades and synergistic grid-transportation solutions such as managed charging and strategic charger placement. Fully implementing such strategic planning and control methods—including business models and mechanisms to engage and compensate consumers—can minimize or even eliminate required grid upgrades. Moreover, there are also opportunities for EV charging to support the grid by helping solve existing and emerging distribution system challenges associated with increasing distributed energy resources (DERs) such as solar generation and battery energy storage. This paper reviews the potential impacts of EV charging on electricity distribution systems and describes methods from the literature to efficiently integrate EVs into distribution systems. Recent Findings Recent work has begun to extend beyond earlier efforts with limited adoption, simple controls, and mostly plug-in hybrid EVs and short-range EVs to look at high adoption rates of long-range EVs, larger distribution test systems, and more advanced EV charge control methods. In addition, increased interest and viability of higher power charging have prompted several studies showing how adverse impact from EV charging on electricity distribution systems can be exacerbated by high-power charging levels and concentrated EV adoption in certain areas. There has also been considerable recent effort to look at bulk transmission-level impacts of widespread EV adoption, which often includes a brief mention of distribution concerns, while also highlighting EV-distribution interactions as a key need for future work. Summary The additional loads from widespread EVs could require costly upgrades to maintain distribution system reliability; however, careful planning and advanced operations strategies can reduce or eliminate such upgrade needs. Moreover, EV charging infrastructure can also support grid stability and improve distribution systems especially when paired with distributed solar, storage, or when equipped with smart charge management and grid-interactive support. Previous studies have found that impacts of unmanaged charging include limited load hosting capacity, transformer and line overloads, and voltage and power quality degradation. Past studies have also explored a wide range of opportunities to mitigate these impacts, including traditional upgrades, enhanced controls, and market design. However, the smaller-scale of most past studies limits their ability to capture impacts and opportunities introduced by managed EV charging, regional-scale movement of EVs, and more widespread EV deployment. With accelerated EV adoption driven by sustained technology progress, policy support, and rapid charging infrastructure deployment, it will become increasingly important to capture entire regions, rather than a single or a few feeders; to advance theoretical control developments to be simulated against more realistic and diverse distribution systems and to advance to widescale field deployments; and to develop more holistic approaches which incorporate EV charging alongside a variety of distributed energy resources. This will require enhanced collaboration across multiple disciplines to develop cost-effective and reliable solutions for the combined mobility and electricity systems of the future.

Water distribution systems (WDSs) utilize battery‐powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real‐time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing‐based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.