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Abstract This research proposes an optimisation model for identifying the optimal locations and sizes of public charging stations for large‐scale areas based on geographic information system (GIS) data and grid partitioning. A grid partitioning technique is utilised to generate subareas and investigate the demand for electric vehicle (EV) chargers calculated based on the number of EVs in each subarea. The optimal locations are formulated as a maximum covering location problem and solved by integer programming to ensure that all vehicle access subareas, according to the GIS data, are covered by charging stations. The sizes of charging stations are formulated as a knapsack problem and solved by the EV‐charge point ratio to guarantee each subarea has the appropriate number of chargers. The Voronoi diagram is integrated to estimate vehicle density and improve visualisation. The model is extended to include potential power loss reduction by adjusting the distribution of chargers in relation to the proximity to electrical substations. The developed methodology has been implemented by the whole country area of Thailand, which covers an area of approximately 510,000 km2. The study results are represented in a map blueprint that is very useful in strategic planning to support the so‐called EV30@30 government policy's ambitious national target of 2,050,000 units of EVs in 2030.

The rapid development of power electronics technology makes the traditional grid power generation, transmission, and consumption gradually show the trend of “power electronics”. With the increase in the number of power electronic converters, the accurate electromagnetic transient (EMT) simulation of model power electronic networks is under tremendous computational pressure. Aiming at solving the problem of slow calculation of large-scale power electronics simulation, scientists have widely used parallel computation of sub-networks to accelerate EMT simulation. This paper proposes an automatic grid partitioning algorithm for large power grids based on merging neighbor nodes with the basic principle of the transmission line decoupling (TLD) method. The algorithm can effectively reduce the communication time and the waiting time due to the uneven computational load during parallel simulation. Finally, the method is implemented in the CloudPSS platform with various scales of grids, and the test results show that the algorithm can realize the EMT simulation efficiently and accurately.

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization. [ABSTRACT FROM AUTHOR]

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF‐problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

This research proposes an optimisation model for identifying the optimal locations and sizes of public charging stations for large‐scale areas based on geographic information system (GIS) data and grid partitioning. A grid partitioning technique is utilised to generate subareas and investigate the demand for electric vehicle (EV) chargers calculated based on the number of EVs in each subarea. The optimal locations are formulated as a maximum covering location problem and solved by integer programming to ensure that all vehicle access subareas, according to the GIS data, are covered by charging stations. The sizes of charging stations are formulated as a knapsack problem and solved by the EV‐charge point ratio to guarantee each subarea has the appropriate number of chargers. The Voronoi diagram is integrated to estimate vehicle density and improve visualisation. The model is extended to include potential power loss reduction by adjusting the distribution of chargers in relation to the proximity to electrical substations. The developed methodology has been implemented by the whole country area of Thailand, which covers an area of approximately 510,000 km2. The study results are represented in a map blueprint that is very useful in strategic planning to support the so‐called EV30@30 government policy's ambitious national target of 2,050,000 units of EVs in 2030. [ABSTRACT FROM AUTHOR]

In response to the high uncertainty of large-scale new energy output in the electrical energy system (EES) and the weak controllability of energy output at multiple time scales, this paper proposes a weak grid identification model for transient energy balance in EESs based on grid partitioning, which has an increasingly complex impact on the weak areas of transient energy balance in the sending-end network. First, the accumulation of port energy during transient faults and the propagation mechanism of port energy in the sending-end system were studied, and an EES transient energy propagation mechanism model was established. Then, considering the energy balance support requirements of nodes, an EES grid partitioning model was established. Afterward, based on the characteristics of transient energy propagation and a grid partitioning model, an identification model for weak areas of transient energy balance in EESs was constructed. Finally, based on actual operating data, numerical simulations were conducted, and the results showed that the proposed weak grid identification model for transient energy balance can meet the requirements for transient stability analysis and transient energy balance characteristic analysis during actual operation of power grids.

Fine grid management is one of the important development directions of transportation planning that has not been fully considered in previous literature. This paper explores the application of the fine grid management method in transportation planning. Based on a case study of Chuanhui, China, this paper proposes a data-driven fine grid partitioning method for determining traffic analysis zones (TAZs). The TAZs are partitioned based on quadrilateral and hexagonal grids. This paper also summarizes a set of criteria for evaluating the impact of different fine grid partitioning methods based on the geographically and temporally weighted regression (GTWR) model. The results show that our fine grid partitioning method for determining TAZs based on quadrilateral grids can achieve a relatively low level of predicted value bias and variable correlation degree bias when the number of TAZs is larger, and it has obvious advantages. Finally, policy implications are proposed to promote the refinement of transportation planning. [ABSTRACT FROM AUTHOR]

In order to ensure the security of power grids and control the level of short-circuit currents, a multi-objective optimization method for power grid partitioning is proposed. This method takes into consideration both short-circuit currents and multi-scenario safety constraints. A power grid partitioning optimization model is established to achieve objectives such as minimizing disconnected lines, maximizing safety margins, and ensuring load balance in the main transformers. The model aims to satisfy constraints related to short-circuit current levels, base-case power flow, and N − 1 security. To address the significant deviation in the static security constraint model caused by large amounts of active power losses in large-scale power grids, an improved direct current model is proposed to reduce these errors and meet the accuracy requirements for grid partitioning optimization. Additionally, to adapt to the variability of renewable energy output, an optimization method is proposed, combining three scenarios of renewable energy generation while satisfying short-circuit current and static security constraints. The power grid partitioning model is mathematically formulated as a large-scale mixed-integer linear programming problem, which presents challenges in terms of hardware requirements and computational complexity when solved directly. To mitigate these challenges, equivalent WARD values are assigned to the short-circuit current constraints, base-case constraints, and anticipated fault-induced power flow constraints. Anticipated faults and bottleneck branches are accurately incorporated, and the problem is decomposed into smaller-scale mixed-integer linear programming problems, solved in a stepwise iterative manner. This approach significantly improves computational efficiency and meets the requirements of practical large-scale power grid applications. To validate the proposed model and algorithm, a simulation program is developed using C++, and a simulation analysis of a regional transmission network is conducted. The program ensures the correctness of the proposed model and demonstrates the effectiveness of the algorithm.

In order to ensure the security of power grids and control the level of short-circuit currents, a multi-objective optimization method for power grid partitioning is proposed. This method takes into consideration both short-circuit currents and multi-scenario safety constraints. A power grid partitioning optimization model is established to achieve objectives such as minimizing disconnected lines, maximizing safety margins, and ensuring load balance in the main transformers. The model aims to satisfy constraints related to short-circuit current levels, base-case power flow, and N − 1 security. To address the significant deviation in the static security constraint model caused by large amounts of active power losses in large-scale power grids, an improved direct current model is proposed to reduce these errors and meet the accuracy requirements for grid partitioning optimization. Additionally, to adapt to the variability of renewable energy output, an optimization method is proposed, combining three scenarios of renewable energy generation while satisfying short-circuit current and static security constraints. The power grid partitioning model is mathematically formulated as a large-scale mixed-integer linear programming problem, which presents challenges in terms of hardware requirements and computational complexity when solved directly. To mitigate these challenges, equivalent WARD values are assigned to the short-circuit current constraints, base-case constraints, and anticipated fault-induced power flow constraints. Anticipated faults and bottleneck branches are accurately incorporated, and the problem is decomposed into smaller-scale mixed-integer linear programming problems, solved in a stepwise iterative manner. This approach significantly improves computational efficiency and meets the requirements of practical large-scale power grid applications. To validate the proposed model and algorithm, a simulation program is developed using C++, and a simulation analysis of a regional transmission network is conducted. The program ensures the correctness of the proposed model and demonstrates the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

With the large-scale development and utilization of renewable energy, industrial flexible loads, as a kind of load-side resource with strong regulation ability, provide new opportunities for the research on renewable energy consumption problem in power systems. This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning, aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy, and achieving the safe, stable and economical operation of power grids. Firstly, according to the evaluation index of renewable energy consumption characteristics of line active power, the power grid is divided into several partitions, and the inter-zone tie lines are taken as the optimization objects. Then, on the basis of partitioning, a two-layer active power optimization model considering the power constraints of industrial flexible loads is established. The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day; the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of the minimum operation cost of the partition, so as to reduce the line over-limit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads. Finally, through simulation experiments, it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid. [ABSTRACT FROM AUTHOR]

VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into sub-groups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.

Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure based on an extended weighted network model. An extended adjacency matrix is used to represent an extended weighted complex network model based on coupling strength rather than the conventional adjacency matrix. Meanwhile, we upgraded the Newman fast algorithm of community detection for establishing a novel power grid partitioning algorithm. The electrical coupling strength (ECS) is defined to better reflect electrical characteristics between any two nodes in power grid. Modularity is also redefined as electrical modularity based on ECS. The Newman fast algorithm is upgraded with electrical modularity maximization as the objective to detect functional communities in power grids. A case study on IEEE test systems with 30, 39, 118, 300 buses and one Italian power network demonstrates the rationality of the extended weighted network model and partitioning algorithm.