Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators\' technical advantages across all examined scenarios.

Economic development relies on access to electrical energy, which is crucial for society\'s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia\'s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95-1.025 p.u.). After reviewing the current distribution network\'s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system\'s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Quantifying manganese (Mn) content in solids is critical for understanding its roles in aquatic ecosystems, soils, water treatment plants and distribution systems. No studies have yet used standard Mn oxides to compare the performance of the numerous digestion methods found in the literature. Nine digestion methods (including USEPA 3050B) were compared using four Mn oxides with varying oxidation states. The HCl concentrate (12.4 M) heated to at least at 40 °C provided quantitative digestion of all Mn oxides tested with ≈ 100 % recovery. HCl concentration is important only for MnO2 digestion, while temperature influences both MnO and MnO2 recovery. Complete recovery of various Al, Cu and Fe standard oxides using a 12.4 M HCl digestion at 95 °C. Digestion of environmental samples for Al, Ca, Fe, Mg and Mn content yielded higher metal content using the HCl method (except for Al). HCl 12.4 M digestion provided better performance than other digestion methods found in the scientific literature because of its high reducing capacity. •Most digestion methods found in the literature do not digest all Mn oxidation states.•Hydrochloric acid is shown to be essential to dissolve all oxidation state of Mn oxides.

In newly commissioned drinking-water polyethylene (PE) pipes, biofilm develops on the inner pipe surface. The microbial community composition from colonization to the establishment of mature biofilms is less known, including the effect on the distributed water quality. Biofilm development was followed through 1.5 years in PE-pipe side streams at two locations of a full-scale, non-chlorinated drinking-water distribution system (leaving a waterworks versus 5-6 km from a waterworks) along with inlet and outlet water quality. Mature biofilms were established after ∼8-9 months, dominated by Proteobacteria, Actinobacteria and Saccharibacteria (61-93% relative abundance), with a higher diversity (OTUs/Shannon Index/16S rRNA gene amplicon sequencing) in pipes in the far end of the distribution system. Comamonadaceae, and specifically Aquabacterium (>30% of reads), dominated young (∼1.5-month-old) biofilms. Young biofilms were linked to increased microbiological counts in drinking water (HPC/ATP/qPCR), while the establishment of mature biofilms led to a drop in HPC and benefited the water quality, highlighting the importance of optimizing commissioning procedures for rapidly achieving mature and stable biofilms.

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state\'s electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs\' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

This study presents a novel approach for the optimal placement of distributed generation (DG) resources, electric vehicle (EV) charging stations, and shunt capacitors (SC) in power distribution systems. The primary objective is to improve power efficiency and voltage profiles while considering practical and nonlinear constraints. The proposed model combines competitive search optimization (CSO) with fuzzy and chaotic theory to develop an efficient and effective solution. The use of fuzzy theory in the model enables the identification of optimal locations for DG sources and SCs, leading to significant enhancements in power index, generation, power losses, and system voltage. Moreover, the proposed fuzzy method is employed to determine the best locations for EV charging stations, further optimizing the overall system performance. The theoretical analysis demonstrates substantial improvements in both accuracy and convergence speed, highlighting the robustness of the proposed approach. In addition, the utilization of chaos theory enhances the local search optimization process, making the proposed method more efficient in finding high-quality solutions. To validate the performance of the model, extensive simulations are conducted on a 69-bus distribution system and various test functions. The results consistently reveal the superiority of the proposed method compared to other conventional optimization techniques. The key contribution of this study lies in its development of a comprehensive and efficient approach for the optimal placement of DG, EV charging stations, and SCs in power distribution systems. The integration of CSO, fuzzy theory, and chaotic theory enables the simultaneous consideration of multiple objectives and constraints, resulting in enhanced power dissipation reduction and voltage profile improvement. The obtained results demonstrate the practical applicability and superiority of the proposed method, which can significantly benefit power system planners and operators in real-world scenarios.

Increases in phosphate availability in drinking water distribution systems (DWDSs) from the use of phosphate-based corrosion control strategies may result in nutrient and microbial community composition shifts in the DWDS. This study assessed the year-long impacts of full-scale DWDS orthophosphate addition on both the microbial ecology and density of drinking-water-associated pathogens that infect the immunocompromised (DWPIs). Using 16S rRNA gene amplicon sequencing and droplet digital PCR, drinking water microbial community composition and DWPI density were examined. Microbial community composition analysis suggested significant compositional changes after the orthophosphate addition. Significant increases in total bacterial density were observed after orthophosphate addition, likely driven by a 2 log 10 increase in nontuberculous mycobacteria (NTM). Linear effect models confirmed the importance of phosphate addition with phosphorus concentration explaining 17% and 12% of the variance in NTM and L. pneumophila density, respectively. To elucidate the impact of phosphate on NTM aggregation, a comparison of planktonic and aggregate fractions of NTM cultures grown at varying phosphate concentrations was conducted. Aggregation assay results suggested that higher phosphate concentrations cause more disaggregation, and the interaction between phosphate and NTM is species specific. This work reveals new insight into the consequences of orthophosphate application on the DWDS microbiome and highlights the importance of proactively monitoring the DWDS for DWPIs.

As drinking water travels from its source, through various treatment processes, hundreds to thousands of kilometres of distribution network pipes, to the taps in private homes and public buildings, it is exposed to numerous environmental changes, as well as other microbes living in both water and on surfaces. This review aims to identify the key locations and factors that are associated with changes in the drinking water microbiome throughout conventional urban drinking water systems from the source to the tap water. Over the past 15 years, improvements in cultivation-independent methods have enabled studies that allow us to answer such questions. As a result, we are beginning to move towards predicting the impacts of disturbances and interventions resulting ultimately in management of drinking water systems and microbial communities rather than mere observation. Many challenges still exist to achieve effective management, particularly within the premise plumbing environment, which exhibits diverse and inconsistent conditions that may lead to alterations in the microbiota, potentially presenting public health risks. Finally, we recommend the establishment of global collaborative projects on the drinking water microbiome that will enhance our current knowledge and lead to tools for operators and researchers alike to improve global access to high-quality drinking water.

The quality of water changes from source to tap, presenting challenges in maintaining consistent water quality across the system. Predicting water quality in distribution systems, including disinfectant residual loss and by-product formation, has been the subject of research since the early 1990s. Although numerous models have been proposed to predict residual chlorine decay, disputes exist among researchers and experts over the superiority of certain models. Accordingly, this study modified the existing process-based bulk decay models by replacing the initial Total Residual Chlorine (TRC) concentration parameter with TRC demand, leading to an improvement in the models\' performance. The modification resulted in a 38.03%, 28.02%, 23.11%, and 33.29% average improvement in Mean Squared Error (MSE) values for the First Order Model (FOM), Parallel First Order Model (PFOM), Second Order Model (SOM), and Parallel Second Order Model (PSOM), respectively. The study also introduced an online predictive method based on a Machine Learning (ML) algorithm that predicts the first-order TRC bulk decay rate by using water quality parameters as inputs. A Gaussian Process Regression (GPR) model was used to predict the kinetic parameters in FOM, which accurately predicted the test sets for most of the cases. In addition, a new methodology was proposed in this study for predicting TRC in water distribution systems that incorporates the variability of source natural organic matter, operational actions, and water demands. This method seeks to develop high-fidelity and robust water quality predictions that provide operational decision support for optimized distribution system management. In conclusion, this study emphasizes the importance of understanding water quality changes from source to tap and the challenges of maintaining consistent water quality across the system. The study suggests modifying existing models and introducing a novel methodology for predicting residual chlorine in water distribution systems that can improve water quality management and, ultimately, better public health outcomes.

The integration of Distributed Generators (DGs) into distribution systems (DSs) leads to more reliable and efficient power delivery for customers. However, the possibility of bi-directional power flow creates new technical problems for protection schemes. This poses a threat to conventional strategies because the relay settings have to be adjusted depending on the network topology and operational mode. As a solution, it is important to develop novel fault protection techniques to ensure reliable protection and avoid unnecessary tripping. In this regard, Total Harmonic Distortion (THD) can be used as a key parameter for evaluating the grid\'s waveform quality during fault events. This paper presents a comparison between two DS protection strategies that employ THD levels, estimated amplitude voltages, and zero-sequence components as instantaneous indicators during the faults that function as a kind of fault sensor to detect, identify, and isolate faults. The first method uses a Multiple Second Order Generalized Integrator (MSOGI) to obtain the estimated variables, whereas the second method uses a single SOGI for the same purpose (SOGI-THD). Both methods rely on communication lines between protective devices (PDs) to facilitate coordinated protection. The effectiveness of these methods is assessed by using simulations in MATLAB/Simulink considering various factors such as different types of faults and DG penetrations, different fault resistances and fault locations in the proposed network. Moreover, the performance of these methods is compared with conventional overcurrent and differential protections. The results show that the SOGI-THD method is highly effective in detecting and isolating faults with a time interval of 6-8.5 ms using only three SOGIs while requiring only 447 processor cycles for execution. In comparison to other protection methods, the SOGI-THD method exhibits a faster response time and a lower computational burden. Furthermore, the SOGI-THD method is robust to harmonic distortion, as it considers pre-existing harmonic content before the fault and avoids interference with the fault detection process.