Photovoltaic Distributed Generation Research Articles

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Compressor speed control for optimizing energy matching of PV-driven AC systems during the cooling season

Photovoltaic-driven air conditioning (PVAC) systems innovatively utilize PV power for building cooling, reducing PV power fluctuation impact on the utility grid. This study introduced a novel PVAC system that optimizes the energy matching between PV generation and air conditioning electricity demand through directly controlling of the compressor speed in accordance with the real-time PV power generation. A PID control method was employed to regulate the compressor speed of PVAC, and three control strategies were proposed and successfully implemented during the experiments. The system's efficiency was evaluated using metrics such as the self-consumption rate (SCR), self-sufficiency rate (SSR), real-time zero energy proportion (RZEP), and the mean absolute error (MAE) of indoor temperature. The results of this study confirm that the proposed control method significantly enhances the power matching between the PV power generation and the air conditioning electricity demand. Implementing the recommended strategy, SCR increased from 0.53 to 0.625 and SSR from 0.716 to 0.808, all while maintaining indoor thermal comfort. Moreover, the proposed approach mitigates the impact of distributed PV generation on the utility grid and potentially diminishes energy storage needs, which can contribute to a lower system investment requirements and operating costs.

Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook

This paper proposes a method to assess the financial attractiveness provided by adding a Battery Energy Storage System (BESS) in distributed photovoltaic (PV) generation on public buildings in Brazil. The method is applicable to prosumer units (PU) connected on the medium voltage grid operating under time-based electricity tariffs. The BESS primary objective was to achieve optimal reductions in electric energy expenses through effective energy arbitrage mechanisms. The procedural steps of the methodology begin with an analysis encompassing assessment of solar resource, PU consumption profile, and BESS sizing and operation. Subsequently, steps entail contracted power optimization, PU net-metering analysis, and evaluation of BESS impacts on electric energy expenses. Lastly, a regulatory and economic analysis is conducted incorporation considerations on BESS taxation, behind-the-meter regulations, and a sensitivity assessment extending to the 2030 outlook. By 2050, it is anticipated that PV generation will surpass hydropower, becoming the predominant component of Brazil's energy mix. This shift is expected to create an increased need for solutions, notably storage systems, capable of meeting flexibility requirements and maintaining grid resilience. The results suggest that the financial viability of incorporating BESS becomes favourable when the battery cost is below 365 $/kWh. In approximately 50% of the Brazilian territory, prevailing economic conditions support the adoption of BESS. Nationwide feasibility of integration is anticipated for the year 2027. It was observed that government policies to exempt BESS taxation, even if temporary, would be extremely interesting to promote the widespread adoption of this technology. The 2030 outlook of the transition to these benign renewable energy technologies is already in place, and will dominate the energy mix.

Optimum ground-mounted on-grid connected photovoltaic system

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively.The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis.The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km2) compared to the traditional criterion (76.28 km2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden.The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

Impact on on-load TAP-changer transformers in industries due to high penetration of photovoltaic distributed generation

Impact on on-load TAP-changer transformers in industries due to high penetration of photovoltaic distributed generation

Dynamic fuzzy learning based hybrid GWO-CSA for optimal planning of PV, BESS and DSTATCOM with network reconfiguration

The proposed approach is on the optimal planning issue of photovoltaic distributed generation (PV-DG) and DSTATCOM device with integrated battery energy storage systems (BESS) considering network reconfiguration by applying hybrid Grey-Wolf crow-search algorithm (GWO-CSA) encompassing dynamic fuzzy learning (DFL) optimization technique. Considering the stochastic nature of solar irradiance and variations, when solar energy is inadequate, BESS acts as backup energy storage device to meet essential load requirements. Network reconfiguration is to reduce power loss by changing the network tie switches using optimization algorithm. The principal objective of the proposed research work is to reduce the total power loss, enhance the voltage profile, improve the voltage stability index, and perform network reliability analysis. The beneficial effect of the proposed method is validated on Standard IEEE 69 and Standard 118 bus systems. It found that for the IEEE 69 bus system, the overall Real power loss values decreased to 12.82% and overall Reactive power loss increased to 78.18%. The total voltage deviation index (TTVDI), and total voltage stability index (TTVSI) values decreased to 17.94% and − 0.75% respectively. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 56%, 3.54%, − 3.09%, 0.55% and 30% respectively. Similarly, for IEEE 118 bus system overall Real power loss, overall Reactive power loss, TTVDI, and TTVSI decreased to 9.53%, − 0.11%, 24.64%, and 3.87%. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 2.4%, 1.0%, 1.54%, 0.94%, and 10.1% respectively. Further, the proposed approach of DFL-based hybrid GWO-CSA considers the reconfiguration with PV-DG, DSTATCOM and BESS enhances the overall performance compared to other scenarios.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the “dual carbon goals,” there has been a robust development of distributed photovoltaicpower generation projects in the promotion of their construction. As part of this initiative, a comprehensive and systematicanalysis has been conducted to study the overall benefits of photovoltaic power generation projects. The evaluation processencompasses economic, technical, environmental, and social aspects, providing corresponding analysis methods and datareferences. Furthermore, targeted countermeasures and suggestions are proposed, signifying the research’s importance forthe construction and development of subsequent distributed photovoltaic power generation projects.

Multi-Objective Optimal Allocation of Hybrid Photovoltaic Distributed Generators and Distribution Static Var Compensators in Radial Distribution Systems Using Various Optimization Algorithms

In recent years, considerable growth was about the integration of renewable energy sources in the Radial Distribution Systems (RDS), as Photovoltaic Distributed Generators (PVDG) due to their importance in achieving plenty desired technical and economic benefits. Implementation of the Distribution Static Var Compensator (DSVC) in addition to the PVDG would be one of the best choices that may provide the maximum of those benefits. Hence, it is crucial to determine the optimal allocation of the devices (PVDG and DSVC) into RDS to get satisfactory results and solutions. This paper is devoted to solve the allocation problem (locate and size) of hybrid PVDG and DSVC units into the standards test systems IEEE 33-bus and 69-bus RDSs. Solving the formulated problem of the optimal integration of hybrid PVDG and DSVC units is based on minimizing the proposed Multi-Objective Function (MOF) which is represented as the sum of the technical-economic parameters of Total Active Power Loss (TAPL), Total Reactive Power Loss (TRPL), Total Voltage Deviation (TVD), Total Operation Time (TOT) of the overcurrent relays (OCRs) installed in the RDS, the Investment Cost of PVDGs (ICPVDG) and the Investment Cost of DSVCs (ICDSVC), by applying various recent metaheuristic optimization algorithms. The simulation results reveal the superiority and the effectiveness of the Slime Mould Algorithm (SMA) in providing the minimum of MOF, including minimization of the power losses until 16.209 kW, and 12.11 kVar for the first RDS, 4.756 kW and 7.003 kVar for the second RDS, enhancing the voltage profiles and the overcurrent protection system. Moreover, the ability to reach the optimal allocation of PVDG and DSVC and maintain the voltage profiles in the allowable limit, whatever the load demand variation.

Research on Phase-to-phase Fault Location Method of Active Distribution Network

Due to the weak feed characteristic of active distribution networks, the sensitivity of the original fault phase selection method and the accuracy of fault location are decreased. Most of the existing fault section location methods focus on the phase selection and location of the earth fault, and there is a lack of relevant research on the location of the fault section to the phase-to-phase fault. Moreover, most of the methods need to install the signal measuring device in the system as large as possible, which is not economical. The operation condition of active distribution networks is complicated, the fault characteristics are weak, and the location result is easily affected by non-fault disturbances such as DG access capacity and location. In this paper, the phase-to-phase fault of active distribution networks is taken as the research object. Firstly, phase current and zero-sequence current combined criterion is used for fault phase selection, and then combined with the phase-frequency characteristics of mode impedance of distribution line and photovoltaic power line, a fault section location method based on transient characteristic frequency band is proposed, which is not affected by non-fault disturbance such as photovoltaic control strategy and DG access capacity and location. Simulation results show that the method is effective.

Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards.

Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection

Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best location of the meeting points in the road network, and finally, the patrol path with the meeting points is optimized twice. Through the simulation test of the standard data set and real data, the effectiveness of the proposed method in solving the path planning problem of distributed photovoltaic coordinated inspection by ground-to-air robots is verified.

Allocation of PV Systems with Volt/Var Control Based on Automatic Voltage Regulators in Active Distribution Networks

This paper presents an optimal allocation methodology of photovoltaic distributed generations (PVDGs) with Volt/Var control based on Automatic Voltage Regulations (AVRs) in active distribution networks considering the non-dispatchable mode of PVDG operation. In the proposed methodology, an intelligent coordinated Var control is activated via controlling the AVR tap position and the Var injection of PV inverters to achieve a compromise between reducing active and reactive power losses and enhancing voltage quality in a distribution network. Also, the scheduled power factor mode of operation is investigated for the PV inverters. Added to that, the proposed allocation methodology is handled on the basis of hourly loading variation under simultaneous control modes of PV inverters and AVR. Moreover, the impacts of the specified number of PVDGs are assessed on the distribution system’s performance. A recent effective optimizer of the slim mold algorithm (SMA) is dedicated to solving the proposed optimization framework. The simulation implementations are executed on a practical distribution network of the Kafr Rabea area related to South Delta Electricity Company in Egypt. Also, the application is conducted for a large-scale distribution network from the metropolitan area of Caracas. The proposed methodology provides superior performance in minimizing the active and reactive power losses and improving the voltage profile.

Application of Internet of Things in Residential Distribution Systems

Enabling an internet of things (IoT) application in residential distribution systems by integrating houses with IoT windows and occupant behavior can provide numerous advantages to the power grid, including, but not limited to, demand diminution, congestion reduction, and capacity deferral. This paper presents a new framework that mathematically enables an IoT application in residential distribution systems by integrating IoT windows and occupant behavior with houses for load management and energy conservation. With the proposed framework, we model residential loads considering the IoT concept, and then develop a mathematical optimization model that facilitates the integration of IoT-based houses into the residential distribution system. Different case studies considering a 33-bus distribution network are presented and discussed to demonstrate the effectiveness of penetrating IoT-based houses on distribution system operations and household profitability. It is observed that the profit of the local distribution company decreases when houses are transformed to IoT-based houses due to the fact that less energy is sold to the households. On the other hand, the operation cost of the IoT-based house is lower than that of the conventional house because of the better-managed house energy use, thereby resulting in saving money. It is found that 10% and 20% penetrations of IoT-based houses help reduce the maximum power imported through the distribution substation by 30 kW and 60 kW, respectively. It is also found that the load of IoT-based houses and power availability of a rooftop photovoltaic generation are not compatible, and hence, without an action from the customer and/or utility to coordinate them through a demand response program, IoT-based houses would not contribute to increasing the connectivity of PV-distributed generation in the smart grid.

Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

Smart Sag Detection and Reactive Current Injection Control for a PV Microgrid under Voltage Faults

Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions.

Investigating the Performance of Non-standard Overcurrent Relay with Integration of Photovoltaic Distributed Generation in Power Distribution System

The electricity demand is increasing daily, so the generation power should be raised to fulfil that demand. Renewable distributed generation-based photovoltaic sources are one of the best solutions to satisfy the Power Distribution System (PDS) as long as the fossil resources are on the verge of extinction. At the same time, connecting the Photovoltaic Distributed Generation (PVDG) to the PDS may cause issues with the system's technical parameters, such as a protection system based on overcurrent relays, unless they are optimally allocated. In this context, this paper will be devoted to optimally allocating multiple PVDG units in the PDS using the Slime Mould Algorithm (SMA), meanwhile studying the impact of that optimal integration on the overcurrent protection system that will be represented and based on various chosen non-standard overcurrent relays (NS-OCRs) which many researchers develop, and trying to figure out and pick up the best type that provides improvement to the protection system including the minor impact on the coordination time interval. To achieve the maximum and best of the requested results, a multi-objective function was proposed to be minimized based on the sum of total active power loss, total voltage deviation, and total operating time of the relays.

Study of reducing losses, short-circuit currents and harmonics by allocation of distributed generation, capacitor banks and fault current limiters in distribution grids

The growing interest for researches that focus on renewable energies, as well as the increase in funding for this sector, is thanks to the advantages of renewable energy namely the reduction of environmental impacts and their great reliability. However, to maintain the correct operation of the electrical grid and its quality, it is essential to study the impacts that these generating sources have on the electrical system. The present research paper presents a novel methodology that considers the optimal allocation of photovoltaic distributed generation, capacitor bank, and fault current limiters reactors in distribution feeders, whose purpose is minimizing simultaneous the short-circuit current, active losses and harmonic distortion rate of distribution feeders. For this, a Multi-objective Gray Wolf Optimize has been applied and tested in three different distribution feeders. The result demonstrated that during the entire optimization process, the greatest reduction of the analyzed parameters, which are losses, harmonic distortion rate and short circuit current are associated with the location and number of devices that are allocated in the system.

Optimizing the hybrid PVDG and DSTATCOM integration in electrical distribution systems based on a modified homonuclear molecules optimization algorithm

AbstractThe increasing use of non‐linear loads in electrical distribution systems (EDS) led to a greater need for reactive power compensation, losses minimization, improved voltage and stability. This paper proposes the optimal integration of hybrid photovoltaic distributed generation (PVDG) and distribution static synchronous compensator (DSTATCOM) into IEEE 33 and 69‐bus EDS. A modified version of homonuclear molecules optimization (mHMO) is developed to determine the optimal allocation of the devices, while minimizing a multi‐objective function (MOF) based on total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices (ICPVDG and ICDSTATCOM). The primary objective of the mHMO is to enhance the equilibrium between exploration and exploitation in the original HMO by implementing a fresh exploration stage. The effectiveness of mHMO was assessed using CEC17 benchmark functions. The findings demonstrate that mHMO achieved excellent results, including high‐quality solution and a favourable convergence rate. Additionally, results demonstrate that mHMO outperforms its basic version in reducing TAPL by 94.27% and 97.87% for the two EDS, while improving voltage profiles and reducing the cost of integrated devices. This study shows the potential of hybrid PVDG‐DSTATCOM in improving the performance of EDS and highlights the effectiveness of mHMO in optimizing their integration.