Power Network Research Articles

Short-term Load Forecasting using Genetic Algorithm based Artificial Neural Network

The electrical load Forecasting is considered one of the main and important points for the planning for the future in the electric power networks in both of transmission and distribution systems. In this paper, the genetic optimization algorithm (GA) with the artificial intelligent network (ANN) methods are proposed to forecast the electrical load of the building of Electrical Power Distribution office in the Iraq /Anbar Governorate. Moreover, the optimization method of GA is presented to train the ANN weights using Matlab software with simple programming code. Then, the proposed method is applied to the actual load and the results are compared with that obtained in traditional method such as ANN and regression trees (RG trees) methods. Hence, from the simulation results and the comparison, we can confirm that the proposed method is better than the traditional methods, and it achieved a good agreement with actual load and it provide a good total RMSE of 6% when compared with other methods

Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index

This study aims to analyze the correlations and information flow between a local Sharia-compliant index and global Sharia-compliant indices using Artificial Neural Network (ANN) models, shedding light on the integration of international and national Sharia-compliant markets. Given recent market conditions and uncertainties at both local and global levels, the research investigates the predictive power of ANNs and examines the relationship between the Dubai Financial Market Sharia Index (DFMSI) and three well-known global Islamic indices: the Dow Jones Islamic Market World Index, the Standard and Poor’s Global Sharia Index, and the Financial Times Stock Exchange Sharia All World Index. By incorporating data from multiple global Sharia indices, the study expands data sources beyond the local market, providing a comprehensive analysis of factors influencing the DFMSI’s price. This approach reduces reliance on a single dataset and strengthens the robustness of the research findings. Additionally, the inclusion of the three global Sharia indices offers benefits such as access to global market insights, risk management, and practical applications for investors. The study demonstrates that ANNs, particularly the Nonlinear Autoregressive with External Input Neural Network (NARX), outperform classical models in accurately predicting the DFMSI. This research contributes significantly to the understanding of Sharia-compliant investments by providing a comprehensive analysis of how multiple global indices affect a local market index. It also offers valuable insights for investors and portfolio managers, suggesting that the integration of advanced machine learning models can enhance decision-making processes and improve risk management strategies.

AI-Enabled Ultra-large Virtual Screening Identifies Potential Inhibitors of Choline Acetyltransferase for Theranostic Purposes.

Alzheimer's disease (AD) and related dementias are among the primary neurological disorders and call for the urgent need for early-stage diagnosis to gain an upper edge in therapeutic intervention and increase the overall success rate. Choline acetyltransferase (ChAT) is the key acetylcholine (ACh) biosynthesizing enzyme and a legitimate target for the development of biomarkers for early-stage diagnosis and monitoring of therapeutic responses. It is also a theranostic target for tackling colon and lung cancers, where overexpression of non-neuronal ChAT leads to the production of acetylcholine, which acts as an autocrine growth factor for cancer cells. Theranostics is a hybrid of diagnostics and therapeutics that can be used to locate cancer cells using radiotracers and kill them without affecting other healthy tissues. Traditional virtual screening protocols have a lot of limitations; given the current rate of chemical database expansion exceeding billions, much faster screening protocols are required. Deep docking (DD) is one such platform that leverages the power of deep neural network (DNN)-based virtual screening, empowering researchers to dock billions of molecules in a speedy, yet explicit manner. Here, we have screened 1.3 billion compounds library from the ZINC20 database, identifying the best-performing hits. With each iteration run where the first iteration gave ∼116 million hits, the second iteration gave ∼3.7 million hits, and the final third iteration gave 168,447 hits from which further refinement gave us the top 5 compounds as potential ChAT inhibitors. The discovery of novel ChAT inhibitors will enable researchers to develop new probes that can be used as novel theranostic agents against cancer and as early-stage diagnostics for the onset of AD, for timely therapeutic intervention to halt the further progression of AD.

Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model

This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.

Optimal planning of electric vehicle charging stations and distributed generators with network reconfiguration in smart distribution networks considering uncertainties

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.

The Impact of Post-Furnace Steel Processing Equipment on Reducing Voltage Fluctuations Caused by Arc Furnaces

Arc devices are among the receivers with the highest power connected to power systems. Due to dynamic load changes, these receivers generate a number of disturbances that affect the quality of electric power. The most important disturbances include voltage fluctuations. It is also worth mentioning the asymmetry and deformation of the supply voltage curve. This article discusses the mutual interaction of receivers operating in parallel, operating stably, and devices with dynamic current consumption. Calculations based on model tests and the results of parameters characterizing the quality of energy, which were recorded in the line supplying the steelworks, are presented. The power supply conditions (power of the short-circuit network) were assessed to influence the degree of suppression of voltage fluctuations by loads with stable current consumption.

9.L. Round table: “What” and “How” for building competencies for effective policy making in and for public health

Abstract Policymakers operating within the field of public health must regularly draw on their skills or competencies to facilitate engagement in and between policy sectors and with the public, to navigate the larger landscape of public health institutions and governance levels across the policy cycle, to generate, understand, and apply evidence for decision making, as well as to negotiate the demands of diverse stakeholders. What exactly are the competencies needed and how can they be cultivated to enhance the collective capacity of policymaking institutions? The increasing strain placed on public health systems worldwide, owing specially to challenges in population ageing, the burden of non-communicable diseases, and the exigencies introduced by pandemics, environmental disasters, and geo-political instabilities push us to expand the scope of public health. A look to the policy environs and outcomes seen within and across countries demonstrates that the quality of policy is both highly variable and exceedingly subject to the quality of public health leadership itself. These developments shed light on the need for greater investment in the development of competencies needed by policymakers to engage in effective decision making on key issues impacting population health and the resilience of public health systems. This includes efforts to strengthen the abilities of policymakers to identify problems, recognize opportunities, and put forth solutions in line with robust data and evidence, principles of public health ethics, gaps and priorities in public health needs, and in accordance with financial and political realities. The proposed roundtable will examine the state-of-the-art on policymaking competencies in public health, and how to build them by examining the barriers and opportunities. The panel will convene leading experts in the field of public health research and practice to explore: 1) the conceptual definition of competencies for policymaking at different levels of governance and across the policy cycle; 2) the strengths and limits of competency frameworks and their utility in guiding assessment and training tools, as well as policy practice; 3) the challenges associated with developing, maintaining and transferring competencies in fast-changing, multi-level policy making environments; 4) the challenges for public health experts and institutions to be trusted advisors both by policy makers and other stakeholders and the population; and 5) the opportunities to harness the unexploited power of professional networks in some countries of Europe. Key messages • An enhanced research agenda and investment are needed for the development of competencies - individual and collective - for effective public health policy-making and stronger governance. • Innovative approaches to policy-making are needed, and this requires building competencies, including leadership, for addressing the complex nature of public health.

Analyzing the influence of electric vehicle charging scheduling on distribution transformer lifespan

The ongoing growth in global electricity demand and rapid proliferation of electric vehicles (EVs) are posing challenges to the operation of the low voltage power networks and their components. This study aims to analyze the impacts of peak-load demand on the distribution transformers, considering mild and extreme combinations of electric energy demand and localized charging of plug-in EVs under their different penetration levels, hypothesizing that shifting the demand for charging throughout the day can reduce the aging of distribution transformers. The proposed method employs a fuzzy-logic-based tool, which incorporates the influence of ambient temperature, higher harmonics, reactive power compensation, reverse power flows, and overload. Three different charging/discharging modes have been considered: demand charging, off-peak charging, and synergy of off-peak charging and vehicle-to-house technology. The results indicate that off-peak charging emerges as 2.1 times more effective than demand charging in terms of the transformer's loss of life, particularly for ultimate EV penetration of 100 %. It is demonstrated that the utilization of charging scheduling for EVs should be prioritized to defer costly system upgrades and reinforcements, particularly when EV penetration exceeds 50 %.

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

CAPTURE—Computational Analysis and Predictive Techniques for Urban Resource Efficiency

ABSTRACTMunicipal waste management (MWM) poses significant challenges in the context of rapid urbanisation and population growth. Accurate forecasting of waste production is crucial for designing sustainable waste management strategies. However, traditional forecasting methods often struggle to capture the complexities of waste generation dynamics. This paper proposes a novel methodology leveraging deep learning techniques to forecast municipal waste production. By harnessing the power of deep neural networks, our approach transcends the limitations of conventional models, providing more accurate and impactful predictions. We integrate heterogeneous data sources, including demographic and territorial information, into a comprehensive graph representation of municipalities. Graph Neural Networks are then employed to extract intricate spatial and temporal patterns from the graph structure. Empirical validation through a case study in the Apulia region demonstrates the effectiveness of our methodology in furnishing accurate forecasts for waste production. Our framework is adaptable and scalable, making it suitable for application across diverse geographical areas. This research contributes to advancing waste management practices by providing stakeholders with actionable insights for informed decision‐making.

Harnessing network power: Weaponised interdependence in global tax policy

AbstractFor decades, global tax policy has grappled with the challenge of mobile capital, failing to harness the regulatory potential of network power. This oversight has allowed tax havens to flourish, and multinational corporations and wealthy elites to circumvent state authority, undermining national fiscal policies and democratic capacity to meet grand societal challenges, such as climate change and inequality. But recent regulatory innovations pave a different way forward. This article spotlights the dynamics of ‘weaponized interdependence’, suggesting a critical role for strategically targeting global corporate and financial networks in reinvigorating action against tax havens, tax avoidance and tax evasion. Identifying and exploiting network chokepoints – financial institutions, corporate subsidiaries and expert intermediaries – can empower states to reassert their sovereignty and counterbalance the structural advantages of global capital. This approach helps us understand how and why recent regulatory measures have effectively countered financial secrecy, profit shifting and sanctions evasion, repairing public finances and restoring democratic integrity. In an age where weaponised interdependence is predominantly linked to national security, this recasting of global tax policy highlights a progressive path for using network power to develop effective global economic frameworks, strengthen democratic capacity and address contemporary challenges.

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

Resilience enhancement for interdependent power systems by AI-assisted disaster response

The failure of urban Electrical Distribution Systems (EDSs) can lead to direct or indirect consequences for their interdependent critical infrastructures, for example, the hospitals, the fire department, etc., negatively affecting the well-being of citizens in the city. To improve the resilience of the coupled power network, the Operational Interdependence Simulator (OIS) is proposed to help prioritize the queue of failure recovery and Minimum Spanning Tree and Shortest Path Tree topology reconfiguration to minimize critical load downtime. Additionally, AI agents are introduced to assist decision-making processes. By utilizing machine learning and training in thousands of offline scenarios, the system can be used for real-time calculation of the optimal recovery paths when given the number and location of electrical faults. The IEEE 70-node distribution system case validated the high prediction accuracy and post-disaster topology reconfiguration while considering the interdependencies.

TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks

This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a novel graph autoencoder model specifically designed for MTS learning tasks. The underlying approach of TVGeAN is to combine the power of complex networks in representing time series as graphs with the strengths of Graph Neural Networks (GNNs) in learning from graph data. TVGeAN consists of two new main components: TVG which extend the capabilities of visibility graph algorithms in representing MTSs by converting them into weighted temporal graphs where both the nodes and the edges are tensors. Each node in the TVG represents the MTS observations at a particular time, while the weights of the edges are defined based on the visibility angle algorithm. The second main component of the proposed model is GeAN, a novel graph attention mechanism developed to seamlessly integrate the temporal interactions represented in the nodes and edges of the graphs into the core learning process. GeAN achieves this by using the outer product to quantify the pairwise interactions of nodes and edges at a fine-grained level and a bilinear model to effectively distil the knowledge interwoven in these representations. From an architectural point of view, TVGeAN builds on the autoencoder approach complemented by sparse and variational learning units. The sparse learning unit is used to promote inductive learning in TVGeAN, and the variational learning unit is used to endow TVGeAN with generative capabilities. The performance of the TVGeAN model is extensively evaluated against four widely cited MTS benchmarks for both supervised and unsupervised learning tasks. The results of these evaluations show the high performance of TVGeAN for various MTS learning tasks. In particular, TVGeAN can achieve an average root mean square error of 6.8 for the C-MPASS dataset (i.e., regression learning tasks) and a precision close to one for the SMD, MSL, and SMAP datasets (i.e., anomaly detection learning tasks), which are better results than most published works.

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.

Design and Analysis of Sierpinski Fractal Antennas for Millimeter‐Wave 5G and Ground‐Based Radio Navigation Applications

ABSTRACTThe paper describes compact fractal antennae combined with ground defected structures (DGS) and substrate integrated waveguide (SIW) techniques for enhancing resonate and multiband behavior concerning bandwidth, gain, and radiation in the desired mm‐wave region. Based on the proposed antennae, multiple good notches over wide bands depict a good radiation pattern. The antennae's ultra wide bandwidths (UWB) are 16, 16, and 11.4 GHz concerning resonant frequencies 26.34, 30.35, 34.5, 36.65 GHz; 25.8, 32.2, 35.3 GHz; 30.22, 37.68 GHz, of concerning antennae ant 1 (FA1), 4 (FA2), and 6 (FA3), respectively. The proposed antenna (FA3) with the SIW technique has a peak gain of 5.9 dBi and a peak front‐to‐back ratio of 20 dB, respectively. The proposed antennas are targeted to cover the practical contribution aspect, including advanced 5G bands support (24–40 GHz), broadband spectrum, frequency agility, low 3 dB beamwidth, interference reduction, and medical application suitability, respectively. The proposed antennae also cover the desired mm wave 5G region with different resonating notches over ultra‐wide bandwidth, such as n257, n258, n259, n260, and n261, and ground‐based radio navigation applications. Results are validated with vector network analyzer, spectrum analyzer, and power sensor in the presence of absorber, respectively.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO2 emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.