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New remote sensing technologies, and the meter‐scale geospatial data they create, now allow for detailed urban landscape characterization, thereby advancing grid‐based hydrodynamic models. However, using a uniform fine grid over urban catchments generally result in dense grids and can lead to prohibitive computational costs. Moreover, an inability to see below the water surface and measure river bathymetry in most terrain remote sensing can severely impact local‐scale river hydraulics calculations given the significant volume of water conveyed by the channel. This paper introduces a super grid channel model which allow river channels with any width above that of the grid resolution to be simulated in 1D manner. As an extension of a previous subgrid model, this integration facilitates a seamless transition between subgrid and super grid channels, accommodating situations where channel width may surpass or fall below the grid resolution. The key contribution is the integration of the novel 1D channel representation with a nonuniform structured 2D floodplain hydrodynamic model and then coding this for application on GPU. Compared with the previous pure 2D nonuniform structured approaches, the new model presents an efficient compromise for riverine urban flooding where we are less concerned about fine‐scale details of in‐channel flow. Three tests reveal that the proposed model maintains accuracy but with significantly reduced computational cost. By leveraging GPU architectures, a ∼10× speedup compared to CPU computations is achieved, and a typical 6‐day urban flooding problem (domain size 1.42 km2) at 1 m resolution can be achieved within 10 hr on a single 8 GB GPU. Key Points A nonuniform structured grid with a super grid river channel model is implemented on GPU for efficient meter‐scale urban flood modeling As an extension of the subgrid model approach, this integration allows a seamless transition between subgrid and super grid channels A ∼10× speedup compared to CPU computations is achieved by leveraging GPU architectures

This paper examines the optimal integration of renewable energy (RE) sources, energy storage technologies, and linking Indonesia’s islands with a high-capacity transmission “super grid”, utilizing the PLEXOS 10 R.02 simulation tool to achieve the country’s goal of 100% RE by 2060. Through detailed scenario analysis, the research demonstrates that by 2050, Indonesia could be on track to meet this target, with 62% of its energy generated from RE sources. Solar PV could play a dominant role, contributing 363 GW, or 72.3% of the total installed capacity out of over 500 GW. The study highlights that lithium-ion batteries, particularly with 4 h of storage, were identified as the most suitable energy storage option across various scenarios, supporting over 1000 GWh of storage capacity. The introduction of a super grid is shown to reduce the average energy generation cost to around USD 91/MWh from the current USD 98/MWh. These findings underscore the potential of a strategic combination of RE, optimized energy storage, and grid enhancements to significantly lower costs and enhance energy security, offering valuable insights for policymakers and stakeholders for Indonesia’s transition to a sustainable energy future.

The optimal power flow (OPF) problem is a critical component in the design and operation of power transmission systems. Various optimization algorithms have been developed to address this issue. This paper expands the use of the coronavirus disease optimization algorithm (COVIDOA) to solve a multi-objective OPF problem (MO-OPF), incorporating renewable energy sources as distributed generation (DG) across multiple scenarios. The main objective is to minimize fuel costs, emissions, voltage deviations, and power losses. Due to its non-convex nature and computational complexity, OPF poses significant challenges. While COVIDOA has been utilized to solve engineering problems, it faces difficulties with non-linear and non-convex issues. This paper introduces an enhanced version, the enhanced COVID-19 optimization algorithm (ENHCOVIDOA), designed to improve the performance of the original method. The effectiveness of the proposed algorithm is validated through testing on IEEE 30-bus, 57-bus, and 118-bus systems, as well as a real-world 28-bus system representing Iraq’s standard Iraq super grid high voltage (SISGHV 28-bus). The two-point estimation method (TPEM) is also applied to manage uncertainties in renewable energy sources in some cases, leading to cost reductions and annual savings of ( $70,909.344, $ 817,676.64, and $5,608,782.144) for the IEEE 30-bus, 57-bus, and reality 28-bus systems, respectively. Thirteen different cases were analyzed, and the results demonstrate that ENHCOVIDOA is notably more efficient and effective than other optimization algorithms in the literature.

Climate change mitigation, adaptation to intensifying hurricanes, and decarbonization challenges in developing countries emphasize the urgent need for resilient high-voltage grids to facilitate the expansion of renewables. This research explores the technical feasibility of extending the U.S.–Caribbean Super Grid to include the Virgin Islands, Guadeloupe, Martinique, Trinidad and Tobago, Guyana, Suriname, French Guyana, and the northeastern part of Brazil in South America. This proposed extension aims to capitalize on the recent introduction of a new generation of wind turbines certified for operation under strong hurricane forces. The research utilizes modeling and simulation techniques to evaluate the performance of the proposed extension. A method for modeling and estimating spatiotemporal wind power profiles is applied, and the results demonstrate a reduction in maximum wind power variability within the U.S.–Caribbean Super Grid. Depending on the hurricane trajectory, the variability is reduced from 56.6% to less than 43.2%. This reduction takes effect by distributing peak surplus wind power alongside the proposed U.S.–Caribbean–South America Super Grid (UCASG). The research concludes by acknowledging the merits and limitations of the study and discussing potential directions for future research in this field.

Background One way to design an electricity system wholly based on renewables is referred to as the global Super-grid, a vision of a transmission network of unprecedented geographical scope that uses advanced technology to balance spatially and temporally varying supply and demand across the globe. While proponents, since the 1960s, have argued that a global Super-grid is technologically possible and socially desirable, and significant technical progress has been made since the 1990s, development is slow with new transmission lines being built predominantly with established technology and within the boundaries of single countries. The aim of this study is to explore sociotechnical drivers and barriers of global Super-grid development. Results A main driver is the century old ideas that larger grids are more efficient and contribute to cooperation and peace. Over the last decades, the level of technical knowledge and networks of proponent have grown. The Super-grid also benefits from the potential opportunity of building on existing grids. Barriers stem from the scale of investments needed to experiment, path dependences in established industry and competition from novel smaller scale solutions based on local production, energy storage and smart grid technology. Other barriers originate in the organisational and institutional complexities of international electricity trade, and in the lack of trust at local and global levels, which hinder the development of necessary coordination. Conclusions The analysis suggests that if the Super-grid is to become part of a future electricity system, the discourse needs to open up, move beyond simplistic ideas of efficiency and ‘technocratic internationalism’, and take into account a broader set of social benefits, risks and trade-offs.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system’s required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system’s backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

The concept of a European super-grid for electricity presents clear advantages for a reliable and affordable renewable power production (photovoltaics and wind). Based on the mean-variance portfolio optimization analysis, we explore optimal scenarios for the allocation of new renewable capacity at national level in order to provide to energy decision-makers guidance about which regions should be mostly targeted to either maximize total production or reduce its day-to-day variability. The results show that the existing distribution of renewable generation capacity across Europe is far from optimal: i.e. a 'better' spatial distribution of resources could have been achieved with either a ~31% increase in mean power supply (for the same level of day-to-day variability) or a ~37.5% reduction in day-to-day variability (for the same level of mean productivity). Careful planning of additional increments in renewable capacity at the European level could, however, act to significantly ameliorate this deficiency. The choice of where to deploy resources depends, however, on the objective being pursued-if the goal is to maximize average output, then new capacity is best allocated in the countries with highest resources, whereas investment in additional capacity in a north/south dipole pattern across Europe would act to most reduce daily variations and thus decrease the day-to-day volatility of renewable power supply.

Significance The recent sharp drop in the cost of photovoltaic (PV) electricity generation accompanied by globally rapidly increasing investment in PV plants calls for new planning and management tools for large-scale distributed solar networks. We found that pairs of electricity generation capacity G and storage S , such that S is minimal to provide a given dispatchable electricity capacity for a given G , exhibit a smooth relationship of mutual substitutability between G and S . These G − S isolines support the solution of several tasks. This includes optimizing the size of G and S for dispatchable electricity, optimizing connections between solar parks across time zones for minimizing intermittency, and management of storage in situations of far below average insolation. The recent sharp drop in the cost of photovoltaic (PV) electricity generation accompanied by globally rapidly increasing investment in PV plants calls for new planning and management tools for large-scale distributed solar networks. Of major importance are methods to overcome intermittency of solar electricity, i.e., to provide dispatchable electricity at minimal costs. We find that pairs of electricity generation capacity G and storage S that give dispatchable electricity and are minimal with respect to S for a given G exhibit a smooth relationship of mutual substitutability between G and S . These isolines between G and S support the solving of several tasks, including the optimal sizing of generation capacity and storage, optimal siting of solar parks, optimal connections of solar parks across time zones for minimizing intermittency, and management of storage in situations of far below average insolation to provide dispatchable electricity. G − S isolines allow determining the cost-optimal pair ( G , S ) as a function of the cost ratio of G and S . G − S isolines provide a method for evaluating the effect of geographic spread and time zone coverage on costs of solar electricity.

Line losses in power transmission network establish one of the main problems affecting power generation and distribution systems. Losses have been found to affect the overall efficiency of a system. Therefore, to increase the efficiency of any power system, losses must be minimized and one of the ways of addressing this is through the modelling of Existing 330kV and Meshed 330kV Super-grid Transmission Line for The Nigerian Network. To accomplish this goal, power-flow analysis was carried on existing 330kV and meshed networks using fast decouple techniques via power systems analysis toolbox (PSAT) 2.1.10 in MATLAB 2015a environment. Results from the research work shows an output total loss of 161.372MW/1.757869MW for Existing/meshed 330kV super grid Networks respectively. The results from the study show that the meshed network's has minimal average line loss of 1.2202MW, less reactive power of 0.034MVAR, additional network stabilities with average loops of 41 lines, increase the current carrying capacities of 4·5800MW, better voltage regulations of average 99.976%, high transmission efficiencies of 99.999% ,reduction in the phase shift angles of average 0.03 °C and loss percentage reduction of 98.91% as compared to existing 330kV network of average 10.50 C also meshed networks has overall average voltages between 0.996pu - 1.000p.u. Enactment of meshed network will lead to astonishing power transmission in the future.

With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 × 400 m2 super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.