Optimal Grid Research Articles

Grid Performance Enhancement Study Under Restricted Placement and Sizing of PV Distributed Generation

Increasing power demand and encouragement of renewable energy generation appear the importance of Distributed Generation (DG). The Photovoltaic (PV) is considered a resource of distributed generation. The integration of PV-DG unit into the grid has a significant effect on reducing real power losses, operating costs, and enhancing voltage stability. Most of the current researches handle this issue from the point of optimizing the PV-DG unit size and location to enhance the grid performance. In fact and due to optimal design criteria, PV location and size are selected, implemented, and no luxury for a change. In this research, specific selection of sizing and placement of PV-DG unit in radial distribution systems was previously determined and installed. The main goal of this research is to study the grid performance enhancement under these conditions. IEEE 15 bus system has been taken to perform this study based on a MATLAB environment. The idea of this study is to analyze the grid performance when connecting the PV-DG unit to each bus across the transmission line to find the optimally connected bus considering the available transfer capacity of the existing network. For optimal grid performance enhancement, results show that PV-DG unit fragmentation into small units to be connected to all buses was the best solution.

Numerical Study of Scale Effects on Open Water Propeller Performance

The present study aims to investigate the scale effects of the open water propeller performance using computational fluid dynamics (CFD). The results are presented for the propeller which was previously 3D scanned and digitized. The results obtained using two turbulence models within the numerical simulations are compared. The verification study is conducted to assess the numerical uncertainty and thus obtain the optimal grid size for the numerical simulations. A transition model is used at the model scale to account for the partially laminar flow. The propeller is then scaled, and numerical simulations are performed to assess the scale effects on the open water performance of the considered propeller. The results demonstrate the significant scale effects on open water characteristics however, scale effects are considerably lower when the transition model is applied within the numerical simulations at the model scale.

On the Creation of a Digital Permanently Operating Structural Model for the Sedimentary Cover of the West Siberian Petroleum Province

The paper presents some results of the development of a digital permanently operating structural and geological model (implemented at the Autonomous Institution of the Khanty-Mansiysk Autonomous District–Yugra V.I. Shpilman Research and Analytical Center for the Rational Use of the Subsoil) for the area of central West Siberia and important aspects that need to be considered in implementation practice. The methodological challenges of the variational-grid mapping method and its application include problems such as finding an optimal computational grid and using heterogeneous input information for mapping key seismic horizons. An effective method is considered for using seismic data with uncorrected misties in geological mapping based on corrections for derivatives of the desired function along the seismic profile direction (i.e., seismic surface shape). Special attention is paid to a priori information when mapping stratigraphic boundaries with poorly correlated seismic sections. The conformal thickness model linking geometry of the mapped surface with the structural framework of the two reference boundaries has been tested on the construction of regional structural and geological models for the Jurassic interval of the cross section of West Siberia. The model modification can also be applied to mapping clinoform formations. Given the scale and nontrivial tasks of regional mapping, the issues related to setting up the computing workflow are considered in detail: algorithmization, programming protocols, and automation. In the light of the accentuated relevance of the development of specialized software, the GST (Geo-Spline Technology) software product is considered, with the digital structural framework of the region implemented therein. This resulted in a permanently operating model, since the hierarchical object-oriented approach implemented in GST ensures a complete protocol that integrates computational procedures and data flows, as well as automation of model recalculation. The structural framework serves as the basis for mapping physical properties and parameters, assessment of the region resource potential, and r study of other aspects. The proposed model being not conclusive, it offers rich opportunities for revision and refinement as to the area size (geological model domain), level of detail, database enlargement, thereby determining the directions of further development of the digital structural framework.

Modeling of large-scale integration of agrivoltaic systems: Impact on the Japanese power grid

The emerging potential of agrivoltaics prompted this study. We analyzed the potential value of large-scale integration of agrivoltaic technology in rural farming areas in Japan. Optimal power grid scenarios for agrivoltaics were developed using linear programming. We evaluated the effect of a storage battery system and expanded transmission line capacities using a temporal resolution of 8760 h for all regions in Japan. The output suppression was also investigated when the technology was implemented in rice paddy areas or in equivalent land areas (35% of the total cultivated land; 35TCL). The effects of using batteries and/or expanding the transmission line capacities with different crop densities in each region were analyzed to minimize the total power system costs and to compare overall CO2 emissions. Findings suggest that installation of agrivoltaics is more efficient in the rice paddy field than 35TCL because of the crop distribution across all regions in the country and its proximity to high energy demanded regions. In optimizing agrivoltaic integration, results suggest concurrent utilization of agrivoltaic with expanded transmission lines and utilized battery storage have underscored the beneficial effects of agrivoltaics in power grid planning to efficiently control excess output from agrivoltaics and reduce CO2 emission than utilizing transmission capacity and battery storage with agrivoltaics separately. Concrete spatial plans can ensure the stability of the grid and enhance the integration of renewable energy as a primary power source.

Charging Station Distribution Optimization Using Drone Fleet in a Disaster

A disaster is an unforeseen calamity that can cause damage to properties and can bring about a loss of human lives. Usually, many relief supplies, such as clean water, food, and medical supplies, are required by disaster victims. Quick response and rapid distribution of essential relief items into the affected region can save countless lives and prevent or slow down the effects of disasters. In this regard, disaster management comes into play, which is highly dependent on the topography and access of the disaster-hit area. If the disaster-hit site has little or no road connectivity, the use of UAVs/drones becomes essential in delivering health packages to the affected areas to assist with humanitarian aid. Since the battery capacity of the drone is limited, UAVs/drones require charging stations located at various places to carry out the necessary relief work. These charging stations should be transported using road infrastructure and preinstalled in disaster-prone areas, as access to these areas may be denied once the disaster hits. This article presents a novel optimization model to distribute relief items to disaster-hit areas. The objective of this model is to optimize the location and the number of the charging stations. We consider the relative priority of locations where a preference is given to locations with higher priority levels. The optimal number of charging stations and optimal routes has also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for a different number of targets. Through our numerical simulation, it was observed that the drone’s maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

Non-stationary grid generation algorithm for deformed volumes of revolution

The algorithm is designed for multi-component hydrodynamic simulation and for solving other physical and engineering problems. It is suggested for generating structured grids in the volume of revolution (main body) deformed by another volume of revolution (deforming body). In the deformation process, the deforming body is assumed infinitely stiff and strong (i.e. it does not deform, only the main body deforms). The algorithm is developed within the original variational approach for constructing optimal grids satisfying different requirements. The requirements of closeness of a grid to uniform and orthogonal are considered. The algorithm is performed on the basis of the grid generation technology elaborated earlier within the utilized approach for the volume of revolution. The volume of revolution is obtained by the rotation through 180° around an axis of a plane generatrix curve consisting of segments of straight lines and arcs of circles and ellipses. The technology is fully three-dimensional one and is not reduced to constructing rotational grids obtained by the rotation of two-dimensional grids around the axis. The developed algorithm represents the non-stationary procedure generating three-dimensional structured grids in domains with moving boundaries during which the form of the domain is changing from the volume of revolution to the desired deformed volume. The non-stationary algorithm is an iterative process where, at each stage, the deformation of a grid and then its optimization are carried out. Such algorithm allows to generate grids in very complex geometries. For this there is no need to describe the complex geometry of the deformed domain which is not obvious in the general case. It is necessary to do this only for the volumes of revolutions of the main and deforming bodies by the definition of generatrix curves for them which is essentially easier. It also permits to exclude some other stages of the traditional grid generation for the deformed volume such as generation of an initial grid. The algorithm is realized in the computer code written in C++. Examples of constructed grids are given.

An iterative quota calculation method for congestion management and optimal grid utilization

A quota-based approach can help grid operators deal with congestions in distribution grids in a non-discriminatory way for customers. The quota limits the maximum feed-in or consumption of a group of flexibility providing units (FPU) within a grid cluster. The novel iterative quota calculation (IQC) method is introduced in this paper. In this approach, the maximal active power limits of the FPU are constantly adapted, and the feasible operating region (FOR) of the grid is computed. The quota is computed from the resulting FORs after ensuring that the FPU operation cannot cause grid instability. The proposed method is compared to a previously developed static approach in a real LV grid. Results show the benefits of adding reactive power to the quota calculation, especially in distribution grids with inadequate reactive power compensation.

Wind Energy Analysis and Forecast using Machine Learning

Better prediction tools for future solar and wind power are crucial to reducing the requirement for controlling energy associated with the conventional power facilities. For optimal power grid integrating of highly variable wind power output, a strong forecast is extremely crucial. In this part, we concentration on wind power for the near run projections and conduct a wind unification study in the western United States using data from the National Research Conducted by the university (NREL). Our approach derives functional connections directly from data, unlike physical systems that rely on exceedingly difficult differential calculus. By recasting the prediction problem as a regression problem, we investigate several regression methodologies such as regression models, knearest strangers, and regression algorithms. In our testing, we look at projections for specific machines along with power from the wind parks, proving that a classification algorithm for predicting short-term electricity generation is feasible.

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

A disaster is an unforeseen calamity that causes damage to property or brings about a loss of human life. Quick response and rapid distribution of vital relief items into the affected region could save precious lives. In this regard, disaster management comes into play, which is highly dependent on the topography of the disaster-hit area. If the disaster-hit area has little or no road connectivity, the use of drones in such areas becomes essential for the delivery of health packages. Since the battery capacity of the drone is limited, there is a need of charging stations that should be transported using road infrastructure and pre-installed in disaster-prone areas, as access to these areas may be denied once the disaster hits. In this article, a simulation model was used to optimize the number and location of drone charging stations for deployment in a disaster-prone area in the pre-disaster scenario, aiming at the distribution of relief items to disaster-hit areas in the post-disaster scenario. We consider the relative priority of locations where a preference is given to the locations that have higher priority levels. An optimal number of charging stations and optimal routes have also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for different sizes of the disaster-hit area and the number of targets. In our numerical simulation, it was observed that the drone's maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system’s protection and management. Accurate short-term prediction of a grid-connected PV park’s output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system’s output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park’s output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis.

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach.

Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature.

Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

SummaryHierarchical based routing is a kind of group‐based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large‐scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self‐organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid‐based k‐means clustering protocol (named GBK), which combines grid‐based routing with k‐means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k‐means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBK algorithm named GBK‐R is also proposed to extend network stability of the GBK algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBK and GBK‐R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBK clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.

Collaborative Optimized Operation Model of Multi-Character Distribution Network Considering Multiple Uncertain Factors and Demand Response

As many new devices and factors, such as renewable energy sources, energy storage (ESs), electric vehicles (EVs), and demand response (DR), flood into the distribution network, the characteristics of the distribution network are becoming complicated and diversified. In this study, a two-layer collaborative optimized operation model for the multi-character distribution network considering multiple uncertain factors is proposed to achieve optimal dispatching of ES and EV and obtain the optimal grid structure of the distribution network. Based on basic device models of distribution network, the upper layer distribution network reconfiguration (DNR) model is established and solved by the particle swarm optimization (PSO) based on the Pareto optimality and the Prim algorithm. Then, the lower layer optimal dispatching model of ES and EV is established and solved by the binary PSO. The upper layer model and the lower layer model are integrated to form the collaborative optimized operation model for the multi-character distribution network and solved by iterating the upper and lower layers continuously. A case study is conducted on the IEEE 33-bus system. The simulation results show that the total network loss and the voltage deviation are decreased by 15.66% and 15.52%, respectively, after optimal dispatching of ES and EV. The total network loss and the voltage deviation are decreased by 28.39% and 44.46%, respectively, after the DNR with distributed generation (DG) and EV loads with little impact on the average reliability of the power supply. The total network loss and the voltage deviation are decreased by 26.54% and 27.04%, respectively, after the collaborative optimized operation of the multi-character distribution network. The collaborative optimized operation of the distribution network can effectively reduce the total cost by 114.45%, which makes the system change from paying to gaining.

Adaptive grids for the estimation of dynamic models

This paper develops a method to flexibly adapt interpolation grids of value function approximations in the estimation of dynamic models using either NFXP (Rust, Econometrica: Journal of the Econometric Society, 55, 999–1033, 1987) or MPEC (Su & Judd, Econometrica: Journal of the Econometric Society, 80, 2213–2230, 2012). Since MPEC requires the grid structure for the value function approximation to be hard-coded into the constraints, one cannot apply iterative node insertion for grid refinement; for NFXP, grid adaption by (iteratively) inserting new grid nodes will generally lead to discontinuous likelihood functions. Therefore, we show how to continuously adapt the grid by moving the nodes, a technique referred to as r-adaption. We demonstrate how to obtain optimal grids based on the balanced error principle, and implement this approach by including additional constraints to the likelihood maximization problem. The method is applied to two models: (i) the bus engine replacement model (Rust, 1987), modified to feature a continuous mileage state, and (ii) to a dynamic model of content consumption using original data from one of the world’s leading user-generated content networks in the domain of music.

Fabrication and performances of silver grid transparent conducting films and heaters on glass and PET substrates based on fractal periodic grid pattern design

A metal grid transparent conducting film (TCF) requires an excellent trade-off between optical transparency and electrical conductivity, both of which are affected by grid patterns and parameters. In this work, the fractal geometry was introduced to design fractal periodic grid patterns, which were employed to fabricate fractal periodic Ag grid TCFs with different grid periods, pitches and thicknesses on glass substrates by magnetron sputtering Ag films and laser localized removal. By comparing with the corresponding conventional periodic Ag grid, the fractal periodic Ag grid was confirmed to enable a significant improvement in trade-off between optical transparency and electrical conductivity, and thus excellently improving the comprehensive properties of the TCFs. The optimal fractal periodic Ag grid TCF achieved a figure of merit of 15.04 × 10–2 Ω–1, which was higher than that of the optimal conventional periodic Ag grid TCF (11.38 × 10–2 Ω–1). In addition, transparent heaters were prepared by using the optimal fractal periodic Ag grid TCFs on glass and flexible PET substrates, and showed excellent heating performances, good performance stability and deicing availability. This work provides a simple and extendable method for tailoring optical transmittance and electrical conductivity to fabricate high-performance TCFs.

Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

Wildfires are natural or man-made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two-stage framework for assessing power system-wildfire risk using a data-driven wildfire prediction model. The first stage of the framework estimates the spatio-temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model-predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de-energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio-temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio-temporal risk model for proactive de-energisation against potential power system failure-induced wildfire, and 2) a spatio-temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real-world dataset, is demonstrated on the IEEE 24-bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk-aware operation of the power system enabled by the proposed framework.

Quantifying Urban Expansion from the Perspective of Geographic Data: A Case Study of Guangzhou, China

Understanding and quantifying urban expansion is critical to urban management and urban planning. The accurate delineation of built-up areas (BUAs) is the foundation for quantifying urban expansion. To quantify urban expansion simply and efficiently, we proposed a method for delineating BUAs using geographic data, taking Guangzhou as the study area. First, Guangzhou’s natural cities (NCs) in 2014 and 2020 were derived from the point of interest (POI) data. Second, multiple grid maps were combined with NCs to delineate BUAs. Third, the optimal grid map for delineating BUA was determined based on the real BUA data and applying accuracy evaluation indexes. Finally, by comparing the 2014 and 2020 BUAs delineated by the optimal grid maps, we quantified the urban expansion occurring in Guangzhou. The results demonstrated the following. (1) The accuracy score of the BUAs delineated by the 200 m × 200 m grid map reaches a maximum. (2) The BUAs in the central urban area of Guangzhou had a smaller area of expansion, while the northern and southern areas of Guangzhou experienced considerable urban expansion. (3) The BUA expansion was smaller in all spatial orientations in the developed district, while the BUA expansion was larger in all spatial orientations in the developing district. This study provides a new method for delineating BUAs and a new perspective for mapping the spatial distribution of urban BUAs, which helps to better understand and quantify urban expansion.

Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. We develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

Analysis of the gallium melting problem with different heating configurations

A moving boundary problem is numerically investigated with gallium substance and FVM (finite volume method). Following the determination of the optimal grid size and time-step, a natural convection problem in a vertical slot is studied to validate the multi-cellular melting front. Different heating configurations are modelled to understand the solid-liquid phase change phenomenon under different scenarios for the gallium melting problem. These different scenarios include spatial differential heating (sinusoidal), temporal differential heating (pulsating), inclination angle, and MHD conditions. It is found that grid independence study should not be conducted with liquid fraction or similar average value. If the first time-step is larger than 0.1 s, early development of the Bénard cells will not be captured accurately, even when the outer iterations converge. It is also observed that if peak point of differential heating is close to the top of the domain, melting front will progress faster. When Lorentz force is active, at high Hartmann numbers, oscillation of the Nusselt number is dampened.

Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

An accurate electrical load forecasting is essential for optimal grid operation. The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network: Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Detailed heuristic analysis regarding relevant input feature selection, the volume of training data, hyperparameter tuning and regularizer selection of an optimal LSTM-RNN network configuration is presented. The regularized LSTM-RNN is used to forecast 30-min and 24-h ahead electrical loads of two commercial buildings in Virginia, USA. The forecast is performed for one week each over four different months in 2019: January, April, July and October to represent four different seasons in North America. The performance of electrical load forecasts has been compared against actual smart meter data from the electric utility of these buildings. For the case study presented, Mean Absolute Percentage Error (MAPE%) with the regularized LSTM-RNN is 4.9%, compared to 6.4%, 9.2% and 13.3% with Shallow-ANN (Artificial Neural Network), Support Vector Regression (SVR) and Linear Regression (LR) respectively for 30-min ahead electrical load forecast. For 24-h ahead electrical load forecast, MAPE (%) is 11.6%, compared to 12.7%, 13.4% and 14.3% with shallow-ANN, SVR and LR respectively. The methodology to configure a deep neural network (LSTM-RNN) for electrical load forecasting presented in this paper can be utilized for optimal forecasting performance.