Active Power Losses Research Articles

AI-optimization operation of biomass-based distributed generator for efficient radial distribution system

This research aims to optimize the size and location of biomass-based distributed generator (BMDG) units to enhance the voltage profile, reduce electrical losses, maximize cost savings, and decrease emissions from power distribution systems. Biomass-based distributed generator (BMDG) systems offer numerous advantages to enhance the efficiency of power distribution systems. However, achieving these benefits relies on determining the optimal size and position of the BMDGs. To achieve these objectives, the metaheuristic technique called particle swarm optimization (PSO) is employed to find the optimal placement and size of BMDGs. The proposed model was validated on MATLAB's IEEE-33 bus radial distribution system (RDS), confirming the aforementioned benefits. Comparative analysis between the PSO-based technique and other algorithms from previous research revealed better results with the proposed method. The results indicate that optimal placement and sizing of BMDG units have led to a reduction of more than 67.68% in active power losses and 65.90% in reactive power losses compared to the base case. Additionally, the reduction in active power loss was 40.44%, 11.39%, 42.85%, 1.81%, 0.85%, 29.83%, 5.82% and 28.38% more than artificial bee colony, backtracking search optimization algorithm, moth-flame optimization, Coordinate control, artificial Hummingbird algorithm, variable constants PSO (VCPSO), artificial gorilla troops optimizer (AGTO), and a jellyfish search optimizer respectively. Furthermore, the reactive power losses were reduced by 38.33% and 15.68% compared to VCPSO and AGTO respectively. Furthermore, this study revealed a cost reduction of 6.38% when compared to the AGTO and 1.30% when compared to the AHA. Moreover, the voltage profile of the power distribution system was improved by 7.28%. The presented methodology has demonstrated promising results for BMDGs in RDS across various applications.

Power Flow Analysis in Unbalanced Three-Phase Distribution Systems using Backward/Forward Sweep and Current Injection Methods

The electrical power distribution system is a part of the power system that distributes electricity from the transmission network to customers. In the distribution system, imbalances often occur due to the varying load profiles in each phase. This can cause voltage imbalances in the distribution system. This study aims to compare two power flow analysis methods, Backward/Forward Sweep and Current Injection. The study analyses the voltage and power loss conditions on each phase at each bus and line in the three-phase distribution system under unbalanced conditions. Simulations were conducted on two IEEE test buses, IEEE 19-Bus and IEEE 33-Bus with radial configurations. The power flow calculation results using the Backward and Forward Sweep method showed that in the IEEE 19-Bus system, the highest voltage drop percentage occurred on phase b at bus 19, at 3.14%, the highest voltage imbalance percentage occurred at bus 19, at 0.1409%, and the total active and reactive power losses were 7.352 kW and 3.164 kVAR. In the IEEE 33-Bus system, the highest voltage drop percentage occurred on phase c at bus 18, at 5.85%, the highest imbalance percentage occurred at bus 15, at 0.2077%, and the total active and reactive power losses were 19.107 kW and 8.22 kVAR. The percentage difference between the two methods used is less than one percent, indicating that both methods are sufficiently accurate in analyzing power flow in an unbalanced distribution system.

УНІВЕРСАЛЬНА МАТЕМАТИЧНА МОДЕЛЬ АВТОНОМНОГО АСИНХРОННОГО ГЕНЕРАТОРА З КОНДЕНСАТОРНИМ САМОЗБУДЖЕННЯМ

A universal mathematical model of an autonomous asynchronous generator with capacitor self-excitation in retarded phase coordinates is proposed, taking into account the electromagnetic connections between the windings of the stator and rotor phases, saturation of the main magnetic circuit, and active power losses in the magnetic circuit elements. The model provides an opportunity to analyze stable periodic modes and transient electromagnetic and electromechanical processes in autonomous power supply systems with arbitrary switching schemes of its elements and asynchronous generators. The model provides the possibility of taking into account the residual magnetization of the magnetic circuit of an asynchronous generator and the change in rotor speed during the course of the self-excitation process of an asynchronous generator with a grounded neutral of the stator winding and an asymmetric load. References 10, figures 2.

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-objective-based economic dispatch and loss reduction in the presence of electric vehicles considering different optimization techniques

Currently, electric vehicles (EVs) are the most liked mode for green transportation. However, the vehicle-to-grid (V2G) technology can reduce the peak demand on the power grid, which is an efficient way to encourage the integration of EVs. This paper proposes a multi-objective-based economic dispatch management including EVs to minimize the generator cost and active power loss. The entire system is retained for keeping in mind the economic operation of the whole system. Then, EVs are introduced to the system, taking into account vehicle requirements and load demands and considering EV constraints. The target of the proposed work is to demonstrate how effectively large-scale EVs can participate in valley filling and peak load shaving along with multi-objective-based cost and loss reduction. The proposed optimization problem is employed in an IEEE 30-bus system. The multi-objective grasshopper optimization algorithm and the ant-lion optimization are compared to observe the minimum cost and total loss of the system. The results show that the total generation cost and power loss of the system decrease due to the V2G mode of operation. In addition, EVs provide an alternative method for dealing with peak load, while filling the off-peak hours effectively. The total generation cost and power loss for 24 h using MOGOA without implementation of EVs are 8,757.128 $/hr and 65.28509 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,617.077 $/hr and 55.65349 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 1.59% and the total power loss reduced by 14.75%, and with MOALO, the total generation cost and power loss for 24 h without EVs are 8,977.077 $/hr and 44.20877 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,923.529 $/hr and 41.69524 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 0.59% and the total power loss reduced by 5.68%. The analysis of the results demonstrates how effectively EVs in the V2G mode can reduce the dependency over the grid power during the time of peak load demand.

Solution of the Steiner problem in the search for optimal network structure using population optimization methods

Subject of research: This paper considers an example of solving the Steiner problem for the design of utilities, in particular, power lines. Purpose of research: The study is electrical networks. The study is to assess the suitability of using genetic algorithms to find the network connection scheme with the lowest total active power losses. Methods and objects of research: Heuristic methods of population optimization are applied as a research method. Main results of research: As a result of the work, the suitability of applying genetic algorithms to solve the Steiner problem on the example of an electrical network to minimize total power losses has been evaluated.

Transient stability enhancement of Beles to Bahir Dar transmission line using adaptive neuro-fuzzy-based unified power flow controller

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Methodology for selecting optimal wire grade and cross-section based on an integrated technical and economic criterion

The aim was to develop a methodology for selecting an optimal wire grade and cross-section for overhead power lines with a voltage of above 1 kV under intensive development of power grid systems. This aim was solved using the authors’ previously developed methods: selection of an optimal wire grade based on hierarchy analysis and selection of an optimal wire cross-section by optimizing the specific discounted costs during the entire period of construction and operation of overhead power lines. In the present study, these methods are integrated into a single methodology. It is proposed to implement wire inspections prior to the stage of technical and economic calculations, since the necessary data has already been taken into account during the selection of a wire grade and its cross-section. The proposed methodology was tested on a typical example of reconstruction of the Zapadnaya– Davydovka 110 kV overhead line, which creates limitations in the power supply of Primorsky Krai due to insufficient wire capacity and its operation beyond the normative period. SENILEK AT3/C 150/24 wire was selected as a solution for the example under consideration. The application of this wire grade allows not only the overhead line capacity to be increased by 151% without replacing the existing supports, but also active and reactive power losses in the electric network to be reduced by 18.9 and 2.5%, respectively. The proposed methodology enables selection of an optimal grade and cross-section of any wire design, taking the dynamically changing operational conditions of power grid systems into account. The solutions found by the proposed methodology may contribute to a more efficient operation of power lines due to increasing their transmission capacity, reducing the number of used supports, replacing the line wire without replacing supports, decreasing ice formation on wires, and reducing power losses.

Novel Empress SARANI Optimization Algorithm to solve the Electrical Energy -Active Power Loss Reduction and Voltage Stability Enhancement

; This paper proposes Pomarine jaeger Optimization (PJO) algorithm, Tiger hunting Optimization (THO) Algorithm, Desert Reynard and Vixen Inspired Optimization (DRVIO) Algorithm, Lonchodidae optimization (LO) algorithm, Caracal optimization (CO) algorithm, Barasingha optimization (BO) algorithm, Amur leopard optimization (AO) algorithm and Empress SARANI Optimization Algorithm to solve the active power loss reduction problem. Regular actions of Pomarine jaeger have been emulated to model the PJO procedure. In THO algorithm, how the Tiger moves to capture the prey is imitated and formulated. In DRVIO algorithm, Desert Reynard and Vixen burrowing capability and spurt tactic from desolate slayers are imitated to formulate the algorithm. LO algorithm emulates the physiognomies of convergent progression, track reliance, populace development and rivalry in the growth of the Lonchodidae populace in environment. In CO approach, Caracal assaults the designated quarry and then quests the quarry in a dashing procedure. BO algorithm stimulated by the Barasingha existence capability in the slayer subjugated atmosphere. AO algorithm imitates the Amur leopard behaviour. Movement paths, stalking, breeding and death are the some phases in the Amur leopard life cycle. Empress SARANI Optimization Algorithm is designed by integrating Parastylotermes Empress inspired optimization (PEIO) algorithm, Dryocopus martius optimization (DMO) algorithm, Ostrya Carpinifolia Search Optimization (OCSO) Algorithm, Hermitage Activities Inspired optimization (HAIO) algorithm with SARANI algorithm. Validity of Empress SARANI Optimization Algorithm is verified in 24 benchmark functions, IEEE and Practical systems. Real power loss (MW) obtained by projected algorithms for IEEE 57 bus system is PJO-21.99, THO-22.79, DRVIO-21.79, LO-23.16, CO-23.92, BO-22.81, AO- 24.89 and Empress SARANI- 20.91. For IEEE 300 bus system is PJO-395.153, THO-397.398, DRVIO-394.208, LO-398.192, CO-398.397, BO-395.209, AO-399.884 and Empress SARANI-389.217. For IEEE 354 bus system is PJO-336.108, THO-339.563, DRVIO-339.099, LO-340.164, CO-340.592, BO 338.906, AO-342.184 and Empress SARANI-334.098. For practical system - WDN 220 KV is PJO-29. 008, THO-30. 929, DRVIO-28. 519, LO-31.265, CO-31. 893, BO-29.872, AO-32.899, Empress SARANI-26.101

Reducing Overall Active Power Loss by Placing Solar and Wind Generators in a Distribution Power System using Wild Horse Optimizer Algorithm

This study optimized the locations and sizes of wind-based distributed generators (WDGs) and solar photovoltaic-based distributed generators (PVDGs) to reduce the overall active power loss (OAPL) of an IEEE 85-bus distribution power system (DPS). Three meta-heuristic algorithms, including the Wild Horse Optimizer Algorithm (WHOA), the Archimedes Optimization Algorithm (AOA), and the Transient Search Optimization (TSO) algorithm, were applied and compared to each other to identify the most effective method for finding the best value of OAPL. Based on the analysis, WHOA outperformed the other methods in achieving the best value of OAPL according to different criteria. Additionally, the effectiveness of WHOA was compared with previous studies, while WHOA also proved its strength in reducing overall losses, decreasing grid power, and improving voltage profiles. Moreover, the effectiveness of WHOA was tested for a 24-hour period with varying loads and the addition of PVDGs and WDGs. The results indicated that WHOA could successfully determine the optimal positions of both PVDGs and WDGs in Case 3, Case 4.1, and Case 4.2, achieving the optimal value of OAPL in the selected DPS, decreasing grid power utilization, and improving the voltage profile. In conclusion, WHOA proved itself to be an effective optimization tool for dealing with large-scale optimization problems

Stabilizing Voltage and Managing Power Loss in Medium Voltage Distribution Systems Through Strategic Maneuvers

Repairs and maintenance on the electrical power system often require power outages. To ensure safety and security during maintenance, limiting the extent of these outages is essential. This study aims to analyze the impact of network maneuvers on voltage drop and power loss in feeders in Medan, Indonesia. The research involves simulating the feeder circuit under various maneuver scenarios using ETAP 19.0.1 software to analyze voltage drop and power loss in the 20 kV distribution network. The end voltage on the backup feeder decreased from 20.31 kV before maneuvering to 20.10 kV after maneuvering. Despite the maneuvers causing an increase in voltage drop from 0.724 kV (3.24%) to 1.080 kV (4.23%) and in active power losses from 152.59 kW (2.29%) to 226.92 kW (3.02%), both remained within the acceptable standard of 5%. To mitigate the voltage, drop, the study proposes uprating the conductor by replacing the existing AAAC conductor with an XLPE-insulated cable. This upgrade can increase the end voltage from 20.09 kV to 20.49 kV, thus improving the overall performance of the feeder during maintenance activities.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network

AbstractThe adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

Battery energy storage with renewable energy sources integration in unbalanced distribution network considering time of use pricing

The increasing demand for sustainable energy solutions and the escalating energy demand have facilitated the emergence of renewable energy sources (RES), such as photovoltaic (PV) sources. Combining RES with battery energy storage system (BESS) technology reduces peak hour demand and allows for economical charging and discharging for time-based energy pricing. Integrating PV and BESS in an unbalanced system with multiple RES sources is a challenging task. It requires a robust algorithm to minimise power loss in the distribution system and decrease the voltage unbalance factor (VUF). This paper presents a new multi-objective Pelican optimisation algorithm (MOPOA) for the optimal allocation of PV and BESS. The MOPOA helps find the best placement of PV and BESS in an IEEE-33 bus unbalanced radial distribution system (URDS). The proposed algorithm combines multiple benefits from a lower net present cost (NPC) and a higher voltage profile enhancement index (VPEI). The case studies and simulation results show that the proposed method places PV and BESS in IEEE 33-bus URDS optimally, satisfying all of the system’s requirements. The results indicate an improvement in the minimum VUF factor of 4.4% in a winter day and 4.3% in a summer day; a reduction in active power loss of 16% in a winter day and 7.1% in a summer day; and a reduction in reactive power loss of 7.5% in a winter day and 7.2% in a summer day. Thus, the recommended strategy effortlessly accelerates to a suboptimal solution.

Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system

Distributed Generating Units (DGUs) are widely used in backup power, renewable energy integration, voltage regulation, and energy storage applications. The DGUs are small-scale power-generating units located near the load centers of a power system, as opposed to large, centralized power plants located far away. However, the DGUs must reduce power losses and environmental impacts and improve the voltage profile. This was achieved by effective optimization of the position and rating of DGU. However, the conventional optimization methods failed to optimize the power losses, voltage-current balancing issues, and harmonic distortions. So, this article presents the selection of the best location and size of DGU in radial distribution systems using a nature-inspired multi-objective Harris Hawks optimization (HHO) algorithm while considering improvements in the voltage profile and reductions in active power losses. The HHO algorithm is inspired by the supportive actions of intelligent birds, such as Harris Hawks, while searching for prey. The distinguished hunting process of Harris Hawks using other family members makes it unique, which is useful for searching for better quality solutions while optimizing multi-objective fitness functions. Fitness function is determined by considering each bus's voltage profile and branches' active power losses. The proposed method is tested on two circle distribution systems (69 bus and 118 bus). The results are compared to those obtained using some of the more well-known optimization techniques given by scholars in recent years. The optimization of seven DGU in 118 bus radial distribution systems resulted in 405.32 kW of active power losses, a 68.78% decrease in losses, and 0.9723 Vs of minimum voltage.

Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

AbstractThe prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33‐bus, 69‐bus, and 118‐bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33‐bus, and 69‐bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.