Power Loss Reduction Research Articles

Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources

The integration of renewable energy sources into smart distribution grids poses substantial challenges in maintaining grid stability, efficiency, and reliability due to their inherent variability and intermittency. This study addresses these challenges by proposing a novel two-level optimization model aimed at enhancing operational efficiency and robustness in smart distribution grids. The model synergistically integrates renewable energy sources, energy storage systems, electric vehicles, and demand-side management through a dynamic reconfiguration approach. It employs a robust optimization framework combined with a two-stage second-order cone optimization model to manage real-time operations and strategic grid reconfiguration. Key findings from simulations on the IEEE 33 and 69-bus networks underscore the model’s effectiveness. In the 33-bus system, implementing the demand response program led to a significant reduction in power losses, from 0.64 MW to 0.52 MW, and improved voltage stability, with the minimum voltage increasing from 0.970 to 0.980 p.u. Similarly, in the 69-bus system, power losses decreased from 0.85 MW to 0.79 MW, and voltage stability improved, with the minimum voltage rising from 0.962 to 0.972 p.u. The model also demonstrated reduced energy procurement needs, showcasing its impact on enhancing grid efficiency and reliability. These results highlight the model’s potential for advancing smart grid management strategies, offering significant improvements in operational performance and stability under varying demand conditions.

Efficient power management strategies for AC/DC microgrids with multiple voltage buses for sustainable renewable energy integration

This study proposes a distinct coordination control and power management approach for hybrid residential microgrids (MGs). The method enhances the feasibility of hybrid MGs by reducing power loss on ILBCs. The MG has been modeled with solar and wind generators. The MG comprises multiple direct current (DC) and alternating current (AC) sub-microgrids (SMGs) with varying voltage levels. The coordination control and power management strategies for autonomous hybrid MGs with primary and secondary control levels. A novel technique is proposed to ensure seamless and precise power transfer among SMGs while minimizing the constant operation of ILBCs in islanded mode, with a focus on the secondary control level. The study uses MATLAB/Simulink to analyze on-grid, off-grid, and transient mode power transfer among MG. The MG has been operative during transient/faulty conditions. The results indicate that the proposed method demonstrates excellent adaptability in managing power flow.

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

Experimental investigation on the design of a three-phase power factor corrector utilizing a SEPIC converter

A new configuration for a three-phase electric vehicle (EV) charger based on the Single-Ended Primary Inductor Converter (SEPIC) topology. The proposed charger operates as a power factor correction (PFC) converter, offering both step-up (boost) and step-down (buck) functions, enabling efficient adaptation to different input voltage levels while ensuring a stable DC output. By incorporating closed-loop current control and fast-switching regulation, the system attains unity power factor, which helps to reduce power losses and improve overall power quality. A unique control technique is proposed to regulate input current under unbalanced voltage conditions, ensuring reliable operation and high efficiency even during grid fluctuations. Simulations using MATLAB/Simulink demonstrate that, in unbalanced voltage scenarios, the control method maintain stable input current shaping, highlighting the system’s resilience. The results validate the SEPIC-based EV charger’s ability to consistently deliver efficient charging performance in both balanced and unbalanced voltage conditions, while achieving unity power factor and stable voltage regulation.

Design and Experimental Verification of Electric Vehicle Battery Charger Using Kelvin-Connected Discrete MOSFETs and IGBTs for Energy Efficiency Improvement

This research investigates the advantages of Kelvin-connected 4-pin discrete transistors, both MOSFETs and IGBTs, in onboard battery chargers for electric vehicles. The study compares the standard 3-pin and the extended 4-pin packages based on averaged data collected from leading manufacturers. The investigation shows significant potential power loss reduction, thermal operation mitigation, and reduced gate-drive oscillation for the 4-pin package. The benefits have been quantified by analysing the operation of actual switches in an automotive battery charger based on Boost-PFC and DC-DC LLC converters. The converters’ practical design demonstrates a procedure for integrating the Kelvin-connected package into the design methodology. The results have been verified experimentally.

Integration of Photovoltaic and DSTATCOM In the Distribution Network Using Rat Swarm Optimization Technique

Power loss minimization and improved voltage profile have been major challenges faced by the electrical distribution network (DN) mainly because of the long length of the feeders and the high resistance to reactance (R/X) ratio of the DN. A lot of techniques have been investigated to solve these problems. One of the most prominent is the optimal integration of distributed generation (DG) such as photovoltaic (PV) as well as the integration of Distribution Static Synchronous Compensator (DSTATCOM) into the network. The main challenge with this solution has been the determination of the optimal sizes and sites of the DG and/or DSTATCOM. This paper seeks to optimize the simultaneous allocation of multiple DSTATCOMs and PVs in the DN for power loss reduction and voltage profile improvement using the rat swarm optimization (RSO) technique, which is a simple, yet robust optimization technique. The optimization problem is formulated to minimize power loss, voltage deviation index, and maximize the voltage stability index. The IEEE 33 node DN is used as a test network and the simulation results show the effectiveness of the RSO technique in finding the best sizes and locations of the PVs and the DSTATCOMs. The power losses of the network are reduced from 210.996 kW, and 143.129 kVAr when there is no DSTATCOM nor PVs in the network to 26.155 kW, and 19.128 kVAr when DSTATCOM and PVs are simultaneously allocated into the network. A remarkable improvement in the voltage profile of the network is also observed with the minimum node voltage being 0.98 p.u. compared to 0.9038 p.u. when there are no DSTATCOMs or PVs. The RSO results were compared with other techniques from the literature, and it proved its superiority.

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Smart Monitoring of Microgrid-Integrated Renewable-Energy-Powered Electric Vehicle Charging Stations Using Synchrophasor Technology

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring.

A comprehensive modeling, analysis, and optimization of two phase, non–isobaric, and non–isothermal PEM fuel cell

This study models a non-isobaric, non-isothermal two-phase flow in a Polymer Electrolyte Membrane Fuel Cell (PEM-FC), focusing on conservation equations for mass, energy, and momentum across its components. Verification involves comparing PEM-FC performance and temperature distribution with experimental and literature data, showing consistent agreements. Results indicate that increasing cathode channel pressure enhances membrane moisture and reduces power loss. Higher oxygen partial pressure improves PEM-FC performance, whereas increased anode channel pressure heightens ohmic losses and lowers output voltage. Temperature distribution reveals highest temperatures near the cathode catalyst layer due to electrochemical reactions. Adjusting pressures in cathode and anode channels affects these temperatures accordingly. PEM-FC power density is optimized using various algorithms, with simulated annealing proving most effective. Optimal values for gas diffusion layer thickness, electrode porosity, and inlet humidity are determined. Under constant current density, power density increases by 6 % compared to baseline conditions, demonstrating effective parameter optimization for enhancing PEM-FC performance.

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance.

The effect of smart transformers on the optimal management of a microgrid

This study examines the impact of smart transformers on the optimal management of microgrids within a combined heat and power framework. Utilizing a Genetic Algorithm for optimization, the research identifies optimal settings for control variables and resource capacities. The integration of smart transformers significantly enhances performance, improving voltage profiles and reducing electrical losses, while minimizing costs and pollution levels.Key gaps in existing literature include insufficient exploration of smart transformers' advantages and a lack of holistic approaches that integrate technical and economic objectives. This study proposes a comprehensive optimization framework that simultaneously addresses multiple goals, such as reducing power losses and environmental impacts.The contributions include the innovative application of smart transformers for accurate control of active power flow and the development of a robust optimization strategy. Comparative analyses with other techniques, such as Particle Swarm Optimization and Interior Point Method, demonstrate the superior performance of the Genetic Algorithm approach in achieving optimal microgrid management.

Optimal‐droop voltage‐restorer for zonal dc distribution system with simultaneous consideration of dynamic current balance and power loss reduction

AbstractA dc voltage restorer (dc‐VR) with optimal droop control is developed to reduce the power loss and solve the dynamic current imbalance simultaneously of the zonal dc distribution system integrating multi‐parallel energy storage. Firstly, the power loss model composed of line loss and converter loss is built and it is proved to be a strictly convex function with respect to droop coefficients, which indicates the power loss can be mitigated by optimizing the current distribution. Thus, an image‐based droop optimization method is proposed to search for the optimal droop coefficients. Then, the economy of voltage compensation on power loss reduction is investigated and an ESS‐combined dc‐VR is designed, including an interleaved parallel architecture and a capacitor‐integrated electric spring (C‐ES). Unified virtual inertia is proposed for interleaved parallel bridge arms to make their dynamic performance consistent. And the newly introduced C‐ES can keep the bus voltage at the rated level to reduce the system cost. Therefore, the problem of dynamic currents imbalance is addressed from the points of converter structure (dc‐VR) and control system (unified inertia), and the power loss can be reduced by voltage recovery (C‐ES) and optimizing the current reallocation which is achieved by updating sharing coefficients from the image‐based droop optimization method. Finally, the simulation cases validate the effectiveness of the proposed optimal‐droop dc‐VR on dynamic current balance and power loss reduction, and hardware in the loop experiment results prove the consistent dynamic characteristics of the dc‐VR's arms.

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.

Atlantic Blue Marlin, Boops, Chironex Fleckeri, and General Practitioner – Sick Person Optimization Algorithms mization Algorithms

In this paper Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm are applied for solving factual power loss reduction problem. The regular actions of Atlantic blue marlin are emulated to design the Atlantic blue marlin (ABM) optimization algorithm and populace in the examination space is capriciously stimulated. Boops optimization (BO) algorithm is designed by imitating the actions of Boops. Boops possess the obliging stalking physiognomies. As a cluster they stalk the quarry by forming the key and subordinate clusters CSO is based on the drive and search behaviour of Chironex fleckeri. Obviously Chironex fleckeri will exploit the limbs to paralyze the pray by injecting the rancour. In real time world a General practitioner will treat the Sick person with various procedures and it has been imitated to model the Projected PS algorithm. In general people will be inoculated and then with respect to disorder and disease- medical treatment will be given by medicines. Inoculation, medicine and operation are the procedures have been considered as the phases of the projected PS algorithm. Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm validated in IEEE 57, 300 systems and 220 KV network. Factual power loss lessening, power divergence restraining, and power constancy index amplification has been attained.

Artificial immune systems (GA-AIS) enabled power loss mitigation in distributed generation: X3PAIS optimization approaches

The distribution network is a crucial component of the power system, with industrialization driving increased energy demand. Traditional power-generating techniques, such as thermal and hydroelectric are not enough to meet this demand, leading to the development of Distributed Generation (DG). DG requires an extensive re-evaluation of the current power system, as it modifies energy losses and line flows. Inadequate integration of DG can cause power outages, disruption of protection coordination, and lead to islanding. AI can help overcome this issue by determining the best system architecture. Researchers have been interested in the Artificial Immune System (AIS) algorithm, which has room for development and lacks a fixed template. In order to improve AIS, X3PAIS, a hybridization strategy that combines clonal selection with a three-parent crossover has been developed within the scope of the study. X3PAIS was pre-tested using applications in a planetary gear train, a wastewater treatment facility, and mathematical calculations, showcasing its robustness and versatility. In the context of power distribution, X3PAIS is used in the multiple DG architecture of the power distribution system, reducing power losses by placing DG units in the best locations and sizing them to match load profiles. The four DGs' experiment results show that X3PAIS can minimize power losses by more than 89 %. To optimize power losses in the power distribution system, X3PAIS may be improved with a three-parent multiple-point crossover operation.

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.

Power capacity enhancement and loss reduction induced by limited solid solubility of Ho 3+ rare-earth substitution in NiCuZn spinel ferrites

Power capacity enhancement and loss reduction induced by limited solid solubility of Ho ³⁺ rare-earth substitution in NiCuZn spinel ferrites

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.

Anomaly Detection Based on Temporal Convolutional Networks in Smart Grid

Abstract Abnormal power consumption detection is an important means to detect power emergencies in time. It can effectively reduce power loss and residential losses due to electrical failures. This paper presents an anomaly detection method based on temporal convolutional networks and statistics. The predicted power consumption results of subsequent periods are compared with the actual results, and statistical methods are used to determine whether abnormal power consumption exists. If an abnormal result occurs, the system will issue a warning, search the fault location in the database established by the user, and determine the initial fault type. Simulation results show that this method can effectively detect abnormal power consumption.

Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation. To illustrate the efficiency and performance of the proposed algorithm, IEEE 14-bus and 30-bus systems are employed. Moreover, the obtained results are compared with those obtained with other three already existing optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). Obviously, the proposed approach can prove the optimal values of controlled variables in solving the ORPD problem by giving the minimum real power loss and voltage deviation than those from compared techniques with less computation time.