Large-scale Distribution Systems Research Articles

Prediction of high latitude nightside Geomagnetic Field Disturbances (GGFDs) during the 9-March-2012 geomagnetic storm using Recurrent Neural Network (RNN)

Geomagnetically Induced currents (GICs) pose a potentially catastrophic threat to the safety and security of large-scale power distribution systems. Using a recurrent neural network, we predict high (≥60°) magnetic latitude (MLAT) and night side (06–18) magnetic local time (MLT) geomagnetic field disturbances (surface dBs/dt and vertical dBz/dt), the trigger of GICs, during a geomagnetic storms occurred on March 09, 2012. We use the high latitude geomagnetic field data from SuperMAG and the solar wind particle and magnetic field data from NASA-OMNIWeb. We divide the high latitude night side of Earth’s globe into multiple sectors and obtain a time series of maximum geomagnetic disturbances observed in each sector. We use the Long Short Term Memory (LSTM) network technique sector by sector on both, the solar wind data and the sectored geomagnetic field disturbance (10° geomagnetic latitude, 1-hour MLT) time series obtained from SuperMAG. Each sector prediction is plotted on a polar map to reveal high latitude night side geomagnetic disturbance patterns. This high latitude nigh side model has great potential. With solar wind input at a Lagrangian-1 point, and geomagnetic field data from SuperMAG, it can predict the peak magnetic disturbance at any given sector for couple of hours in advance. Our machine learning techniques provide the best set of solar wind input that gives the best disturbance prediction. This paper presents a prototype of the global geomagnetic disturbance model with low-resolution spatial grids and a subset of potential solar wind input parameters. Once matured, the global magnetic disturbance model will be provided to the public.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Reducing Water Conveyance Footprint through an Advanced Optimization Framework

This study investigates the optimal and safe operation of pumping stations in water distribution systems (WDSs) with the aim of reducing the environmental footprint of water conveyance processes. We introduced the nonlinear chaotic honey badger algorithm (NCHBA), a novel and robust optimization method. The proposed method utilizes chaotic maps to enhance exploration and convergence speed, incorporating a nonlinear control parameter to effectively balance local and global search dynamics. Single-objective optimization results on a WDS show that NCHBA outperforms other algorithms in solution accuracy and convergence speed. The application of the proposed approach on a water network with two variable-speed pumps demonstrated a significant 27% reduction in energy consumption. Expanding our focus to the multi-objective optimization of pump scheduling programs in large-scale water distribution systems (WDSs), we employ the non-dominated sorting nonlinear chaotic honey badger algorithm (MONCHBA). The findings reveal that the use of variable-speed pumps not only enhances energy efficiency but also bolsters WDS reliability compared to the use of single-speed pumps. The results showcase the potential and robustness of the proposed multi-objective NCHBA in achieving an optimal Pareto front that effectively balances energy consumption, pressure levels, and water quality risk, facilitating carbon footprint reduction and sustainable management of WDSs.

Changes of putative pathogenic species within the water bacterial community in large-scale drinking water treatment and distribution systems

Although the management of microbes in drinking water is of paramount importance for public health, there remain challenges in comprehensively examining pathogenic bacteria in the water supply system at the species level. In this study, high-throughput sequencing of nearly full-length 16S rRNA genes was performed to investigate the changes of the water bacterial community in three large-scale drinking water treatment plants (DWTPs) and their corresponding distribution systems during winter and summer. Our findings revealed significant differences in the bacterial community structure between winter and summer water samples for each DWTP and its distribution management area (DMA). In the groundwater-fed DWTP, selective enrichment of mycobacterial species was observed in both seasons, and the subsequent DMA also exhibited strong selection for specific mycobacterial species. In one of the surface water-fed DWTPs, certain Legionella species present in the source water in winter were selectively enriched in the bacterial community after pre-oxidation, although they were susceptible to the subsequent purification steps. A variety of putative pathogenic species (n = 83) were identified based on our pathogen identification pipeline, with the dominant species representing opportunistic pathogens commonly found in water supply systems. While pathogen removal primarily occurred during the purification processes of DWTPs, especially for surface water-fed plants, the relative abundance of pathogenic bacteria in the DMA water flora was lower than that in the DWTP effluent flora, indicating a diminished competitiveness of pathogens within the DMA ecosystem.

Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program.

Water consumption assessment and classification in industrial buildings - The case study of Continental Advanced Antenna, Vila Real, Portugal

Water abstraction for the industrial, domestic, and agricultural sectors increased from 0.67 trillion m3/year in the 1900s to 3.79 trillion m3/year in 2000, and it is expected to increase by 55 % in 2050 when the majority of the world's population will live in urban areas. Concerning the industrial sector, despite the efforts to a more sustainable use, water is still an essential and irreplaceable resource, and this situation tends to increase due to modern industrial installations that require large-scale and complex water distribution systems. Usually, industries consume a large volume of water, however due to more and severer regulations, it is necessary to propose strategies for the aid of water conservation.This article presents a methodology to analyse, classify and optimize water consumption in industrial installations, using a real case study located in Vila Real, Portugal. The daily water consumption per worker, the daily record of use of each type of device and the hourly usage pattern were analysed and a classification was achieved according to the ANQIP (National Association for Quality in Building Installations) calculator. The case study revealed a great opportunity of improvement in what concerns to water efficient use. Some measures to improve the efficiency of water use in the building were pointed out, such as the use of rainwater harvesting systems, the use of aerators and replacements of taps and faucets when possible.The methodology is expeditious and user-friendly, and easy to replicate in similar cases, however it reveals some difficulties (especially in the data collection process that is made by inquiry, once it is always dependent on the respondent sensibility and willingness).

Assessment and Enhancement of Uncertain Renewable Energy Hosting Capacity With/out Voltage Control Devices in Distribution Grids

Nowadays, with excessive integration of renewable energy sources (RESs), the distribution systems face several challenges due to exceeding the hosting capacity limit (HCL) of RESs. This article proposes a bi-stage approach to maximize the HCL considering RESs and load uncertainties. The first stage develops a multiobjective framework to adjust the system voltage profiles in a specific manner to avoid exceeding the voltage boundaries with high penetration of RESs. In the second stage, two processes are employed to maximize the HCL as the main objective function. In the first one, the photovoltaic (PV) and wind turbine (WT) plants are optimally allocated, while in the second process, a combination of three voltage control devices is proposed. A correlated multidimension probability uncertainty model is used to emulate the uncertainty of PV, WT, and load demand. Manta Ray foraging optimization (MRFO) algorithm is proposed to solve single-objective and multiobjective frameworks in the bi-stage approach considering the performance limits. The proposed approach is tested and validated on medium- and large-scale distribution systems. The effectiveness of the proposed MRFO is approved compared with two optimization algorithms. The proposed approach achieves up to 77.8% RESs integration from the total load demand for the tested systems.

優先度および故障確率を考慮した作業スケジュールに基づいた故障復旧最適化

The service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two-stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage, and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed-integer linear programming problem in the second stage. The computational experiments using a large scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time.

Cooperative Repair Scheduling and Service Restoration for Distribution Systems With Soft Open Points

Fault management is one of the crucial functions of Smart Grid to bring system back to normal operation from fault events. In this paper, we propose a multi-period cooperative solution for the jointly repair and restoration problem in distribution systems (DSs) to minimize the de-energized loads. In the proposed scheme, the novel fully-controlled power-electronic device SOP (soft open point) and fault isolation are included to accelerate system restoration. As the repair and restoration process goes forward, system topology changes frequently. How to determine the control mode of SOPs considering their characteristics during the entire restoration process is rarely studied in the literature. To this end, we present a new virtual power flow based automatic solution to adaptively perform control mode switching for the SOPs in faulted DS. Furthermore, a co-optimization model is formulated that coordinates SOP control, repair scheduling and DS restoration to obtain optimal repair and switch action sequences matching SOP control mode for DS resilience enhancement. The fault isolation is also conducted, since it has great impacts on repair and switch operations and can guarantee the safety of repair crews in practice. Numerical simulations on a modified IEEE 123-node test system and a large-scale actual distribution system verify the effectiveness of the proposed model.

(Micro)Biological Sediment Formation in a Non-Chlorinated Drinking Water Distribution System

Sediment formation in drinking water distribution systems can lead to brown water at customer taps. Previous studies have shown that sediment formation is closely linked with (micro)biological processes in the distribution system, however the mechanism is not fully understood. Most available studies on discoloration or sediment formation mechanism are based on modeling, pilot-scale experiments, or low frequency data collected during pipe flushing. In this study, long-term sediment development in a large-scale drinking water distribution system was studied at one location over 11 years and at several locations along a known water trajectory during one year. Particulate material was collected at several locations using built-in and mobile filters that were connected to transport and distribution pipes in a semi-continuous manner. The volume of the collected material varied seasonally and the highest volumes were collected in the summer season. The material followed similar variations as temperature, invertebrates biomass and concentration of Aeromonas. The results showed that particulate matter of the sediment at downstream distribution locations was not released by the treatment works but instead forms along the distribution network, with increasing particle/floc size, biomass and Fe and Mn content. The large crustacean, Asellus, contributed to material production through feces excretion and formation of detritus by degradation of exoskeletons of dead animals. Detailed chemical characterization of the collected material showed the presence of proteins, calcium carbonate and iron precipitates. A similar sediment composition in a reference distribution system where customer complaints about brown water are experienced less frequently suggests that the sediment formation mechanism is the same but that water quality of the treatment effluent impacts the extent of material formation and growth of invertebrates. Overall, the results indicate that sediment formation in the distribution system is the result of complex combinations of (micro)biological and bio-chemical processes, including aggregation of particles with organic and inorganic matter, microbial growth on particles and biofilm, biomineralization, and growth of invertebrates. The determining factors to limit sediment formation, however, could not be identified. Further research is required to focus on the impact of treatment on shaping the distribution system ecosystem.

Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

Significant opportunity for savings in energy and investment through improved performance of power distribution systems exists in the optimal placement and rating of capacitors, a conventionally cost-effective and popular reactive power compensating technology. A novel optimal capacitor planning (OCP) procedure is proposed for large-scale utility power distribution systems, which is exemplified on an existing utility circuit of approximately 4,000 buses. An initial sensitivity analysis is employed to intelligently reduce OCP computation time and maintain quality of optimal configurations. Three optimization objectives are considered, including the minimization of total system active power losses, standard deviation of node voltages, and investment in total capacitor power rating. Eight multi-objective optimization methods that employ the non-dominated sorting algorithm III (NSGA-III) concept are compared to determine individual merits. Differences between the methods include the incorporation of a penalty constraint for voltage violations and the automatic readjustment of load-tap-changing (LTC) transformer tap settings for proposed capacitor re-configurations concurrently within the optimizer, which ensures peak system performance and fair comparison to the reference case. A multi-step model conversion process was developed with OpenDSS to enable the OCP procedure to be generally applicable to real large-scale utility circuits. OCP is performed for three example sub-circuits served by a substation with a 48MW, 9Mvar peak load, which represents the most extreme case and offers the best opportunity for savings. Example configurations from the resulting Pareto sets through a pseudo-weight vector approach are also analyzed through a systematic procedure of comparison between the most extreme configuration types to inform configuration selection.

Adaptive Particle Filter With Randomized Quasi-Monte Carlo Sampling for Unbalanced Distribution System State Estimation

Particle filter (PF) is an effective state estimation method for nonlinear unbalanced distribution systems with non-Gaussian noises. However, the computational inefficiency limits its real-time application in large-scale distribution systems. This paper proposes an adaptive PF (APF) method by using randomized quasi-Monte Carlo (RQMC) sampling to enhance computational efficiency. Firstly, an RQMC sampling method is introduced to enable uniform sampling based on the low discrepancy Sobol’ sequence. This even sampling accelerates the convergence rate of numerical integrations. Secondly, the standard PF is extended to be an RQMC-PF estimator by using the RQMC sampling instead of the MC sampling. Benefiting from the higher convergence rate, fewer particles are required to obtain precise state estimates, thus notably improving computation efficiency. Thirdly, to further make a trade-off between estimation accuracy and computational efficiency, the number of particles used in the RQMC-PF is adaptively determined with the root mean square of the estimated state covariance, yielding an APF. Test results on unbalanced distribution systems with different scales demonstrate that the proposed method can efficiently provide accurate estimates as well as achieve good scalability.

Service Restoration in Distribution Systems based on Optimal Scheduling of Search for Fault Location and Switching Operation by Crews

The service restoration within a short period, performed when the permanent fault occurs in a distribution system, is essential for high supply reliability. The service restoration can be achieved by searching the fault location and controlling switches to isolate the fault area. However, the service restoration faces difficulties in the uncertainty of the fault location and the multiple crews' scheduling of the switching operations. It is important to search for the unknown fault location efficiently and optimize the crews' scheduling for their service restoration procedures. This paper proposes an optimization method of the service restoration procedures performed by crews considering the search of the unknown fault location by crews to handle the difficulties. The proposed method combines an efficient fault location search with scheduling for multiple crews. We formulate the scheduling problem as a mixed-integer linear programming problem which allows us to schedule the procedures efficiently using optimization solvers. The proposed method is verified by the computational experiments using the large-scale distribution system model with many manual and remote switches.

Temporally-adaptive Robust Data-driven Sparse Voltage Sensitivity Estimation for Large-scale Realistic Distribution Systems with PVs

This letter proposes a new robust data-driven sparse voltage sensitivity estimation approach for large-scale distribution systems with PVs. It has a high statistical efficiency to mitigate the impacts of PV stochasticity and unknown measurement noise under various system operating conditions. A new adaptively-weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> sparsity-promoting regularization is developed, exploiting the temporal characteristic of time-varying sensitivities for better accuracy. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization is used to mitigate collinearity impacts. The Huber loss function and a concomitant scale estimate are adopted to mitigate the impacts of unknown and non-Gaussian noise. These techniques are implemented in a fast recursive parallel computing framework. The proposed estimator is tested by quasi-static time series simulations of a large three-phase unbalanced system with PVs and various discrete time-delayed control devices. Results validate the superior robustness and efficiency of the proposed estimator over existing alternatives.

DC Voltage Oscillation Stability Analysis of DC-Voltage-Droop-Controlled Multiterminal DC Distribution System Using Reduced-Order Modal Calculation

To reduce the dimensions of a multiterminal DC (MTDC) distribution system connected with multiple voltage source converters (VSCs), a dynamic equivalent model is normally established, assuming that the VSCs are either identical or have a specified topology. These assumptions are inconsistent with practical scenarios, and thus, may lead to errors in the analyzed results. In this study, differences of the VSCs are considered, and a dynamic equivalent model of a DC-voltage-droop-controlled MTDC distribution system is established by reconstructing the dynamics of the VSCs and the DC network. Based on that, a dynamic-reconstruction-based reduced-order modal calculation method is proposed to quickly calculate the DC voltage oscillation mode and determine the stability. For a MTDC distribution system with N different VSCs, the highest dimension is NH + T, where the orders of the VSC and DC network are H and T, respectively. The highest dimension using the proposed method can be reduced to H +1. Finally, the advances of the proposed method are demonstrated by comparing with other reduced-order methods. The conclusions are verified by both a simple example and a large-scale DC distribution system with 100 different VSCs.

Cooperative voltage management by demand resources and fair payoff allocation for distribution systems

This paper proposes a socially acceptable framework for the realization of efficient voltage management by cooperation among consumers in electric distribution systems where the frequent voltage variation problem is caused by large amounts of photovoltaic (PV) generation systems. In the proposed framework, the profit maximization problem of the coalition under the constraints to suppress the voltage variations caused by PVs is formulated as a linear programming problem, and the payoff allocation among consumers is provided using a solution concept from cooperative game theory called the core. The proposed method efficiently computes the payoff allocation in the nonempty core to accomplish the cooperative voltage management system. Unlike most related works, this paper employs cooperative game theory to explicitly address incentives for cooperation with the consumers. Although the computation for finding an allocation in the core generally requires a significant amount of time, this paper demonstrates that the allocation can be efficiently calculated by using the dual optimal solution of a formulated linear programming problem. The proposed method is validated by computational experiments on a large-scale distribution system model in which consumers with demand resources (including PVs and controllable loads) are connected.

Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers’ thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.

A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.

Generic Three-Phase Power Flow Formulation for Flexible Modeling and Fast Solving of Large-Scale Distribution Systems

Generic Three-Phase Power Flow Formulation for Flexible Modeling and Fast Solving of Large-Scale Distribution Systems

A two-stage method for optimal placement of distributed generation units and capacitors in distribution systems

This paper proposes a two-stage procedure to enhance the distribution system performance by determining the optimal sizes and locations of distributed generations (DGs) and capacitors considering single and multi-objective functions. In stage-1, two voltage sensitivity factors (VSFs) based on voltage deviation (VD), and voltage stability index (VSI) are proposed to reduce the search space (SS) by selecting the candidate buses for DGs and capacitors placement. In stage-2, the chaotic bat algorithm (CBA) is applied to find the optimal sizes and locations of DGs and capacitors, according to different objective functions (OFs) and system constraints. The considered OFs are real power loss reduction, total VD minimization, and total VSI maximization. The multi-OF, which aims to optimize these objectives simultaneously, is also considered. The load flow calculations are carried out using the backward/forward sweep (BFS) algorithm. The proposed methodology is evaluated and tested on small and large-scale standard test systems, namely 34-bus and 118-bus radial distribution systems, through different case studies. The numerical results obtained by the proposed procedure are compared with other methods in the literature to show the superiority of the proposed procedure for reducing the total real power loss and improving the voltage profile, especially at increasing the power system sizing.