Sizing Of Distributed Generation Research Articles

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.

Optimal Locating and Sizing of DG in Radial Distribution System Using Modified Shuffled Frog Leaping Algorithm

Several advantages have been achieved in distribution systems by adding Distributed Generation (DG). These benefits comprise minimizing power losses and improving the voltage profiles. Such benefits can be enhanced if DGs are optimally sized. This paper presents a DG in the distribution scheme to increase node voltages and reduce the power loss of radial distribution systems by using the Modified Shuffled Frog Leaping Algorithm (MSFLA) technique. The core objective is to decrease active-power losses of the system while keeping the voltage profiles within the defined limits of the system. This paper optimally allocates and sizes three types of DGs in various standard radial test systems and a practical radial feeder. Single and multiple DGs are considered for the proposed work by including some special cases. The standard test systems on which proposed work is performed are 33-bus and 69-bus radial feeders. A practical 43-bus radial feeder of factory area Faisalabad has also been taken under consideration for analysis. The Forward–Backward Sweep (FBS) scheme is selected for the load flow analysis, whereas MSFLA is chosen for finding of optimum size(s) and site(s) of DG(s). Various types of DGs results are retained separately and collectively. We have reduced the losses up to 85.14% and 96.1% for the 33-bus system and 69-bus respectively. The results will depict a significant improvement, both in the minimization of losses and also in improving the voltage profile. The results of multi DGs placement will be much more promising than those achieved after placing them independently.

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).

Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

A Critical Review of Optimization Strategies for Simultaneous Integration of Distributed Generation and Capacitor Banks in Power Distribution Networks

This paper reviews the optimization strategies for the optimal simultaneous allocation of distributed generation (DG) and shunts capacitor banks (SCBs) in electrical distribution networks. These optimization strategies aim to determine the optimal size, location, and combination of DGs and SCBs to constitute a techno-economic system while satisfying the constraints and energy demand of the load. The optimization strategies explicitly reviewed include the problem formulations, optimization techniques, restrictions posed for optimization problems, decision variables, and network operating modes typically assumed while allocating the DGs and SCBs. In addition, there is an attempt to highlight the limitations of the existing literature and future research directions. This study undertakes a comprehensive review of the literature that systematically considers the simultaneous application of DGs and SCBs to advance the existing literature, which lacks such a review. Expectedly, this review will serve as a principle platform for researchers intending to explore the subject area for further improvement.

Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study

Improving the performance of distribution systems is one of the main objectives of power system operators. This can be done in several ways, such as network reconfiguration, system reinforcement, and the addition of different types of equipment, such as distributed generation (DG) units, shunt capacitor banks (CBs), and voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs. This article applies a new optimization technique called RUN to increase hosting capacity (HC) for a rural Egyptian radial feeder system called the Egyptian Talla system (ETS). RUN relies on mathematical concepts and principles of the widely known Runge–Kutta (RK) method to get optimal locations and sizes of DGs, CBs, and VRs. Furthermore, this paper presents a cost-benefit analysis that includes fixed and operating costs of the compensators (DGs, CBs, and VRs), the benefits obtained by reducing the power purchased from the utility, and the active power loss. The current requirements of Egyptian electricity distribution companies are met in the formulated optimization problem to improve the HC of this rural system. Uncertain loading conditions are taken into account in this study. The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area. The introduced RUN optimization algorithm is used to solve the optimal coordination problem between DGs, CBs, and VRs. Excellent outcomes are obtained with a noteworthy reduction in the distribution network power losses, improvement in the system’s minimum voltage, and improvement of the loading capacity. Several case studies are investigated, and the results prove the efficiency of the introduced RUN-based methodology, in which the probabilistic HC of the system reaches 100% when allowing reverse power flow to the utility. In comparison, this becomes 49% when allowing reverse power to flow back to the utility.

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

Performance enhancement of a low-voltage microgrid by measuring the optimal size and location of distributed generation

<p class="Abstract">A power system in which the generation units such as renewable energy sources and other types of generation equipment are located near loads, thereby, reducing operation costs and losses and improving voltage is called a distributed generation (DG), and these generation units are called distributed energy resources. However, DGs must be located optimally to improve the power quality and minimize power loss of the system. The objective of this paper is to propose an approach for measuring the optimal size and location of DGs in a low voltage Microgrid using the Autoadd algorithm. The algorithm is validated by testing it on the IEEE 33-bus standard system and compared with previous studies, the algorithm proved its efficiency and superiority on the other techniques. A significant improvement in voltage and reduction in losses were observed when the DGs are placed at the sites decided by the algorithm. Therefore, Autoadd can be used in finding the optimal sizes and locations of DGs in the distribution system, the possibility of isolating the low voltage Microgrid is discussed by integrating distributed generation units and the results showed the possibility of this scenario during faults time and intermittency of energy time.</p>

Recent Trends in the Optimization of Renewable Distrib-uted Generation: A Review

Distributed energy resources, or distributed generation (DG), are the mainstay of modern power systems aiming towards green energy generation via the effective integration of renewable energy sources. DG involvement in traditional power systems comprises the enhancement of power quality parameters, the utilization of renewable sources, cost optimization, and stable and reliable energy generation. The advantages of such a revolutionary approach can be achieved with the optimal sizing and allocation of DG by means of adequate optimization techniques, constraints, and optimized parameters. In this study, a comprehensive review of DG optimization is presented in light of recent developments, and a comparison is carried out based on the adopted optimization techniques, test system, enhanced parameters, and outcome evaluations.

A Q-learning based electric vehicle scheduling technique in a distribution system for power loss curtailment

Electric vehicles (EVs) are becoming more common in the distribution network (DN), which burdens the system throughout the charging cycle. However, if EVs are used to assist the utility according to its demands, the scenario turns profitable. In this paper, a new methodology to schedule the EVs and distributed generation (DG) according to the DN system demand is proposed. The proposed method is a hybrid of the intelligent Q-learning algorithm, Loss Sensitivity factor (LSF), and Enhanced Grasshopper Optimization (EGOA) techniques. The main objective of this work is to encourage peak shaving, minimize the power loss and improve the voltage profile of overall DN in presence of EVs. The modelling of EVs is done by considering the most significant parameters such as EV State of Charge (EV SoC), travel itinerary, capacity of EV battery, and charging/discharging levels. Initially the location of EV charging stations and DGs are determined using LSF technique. Further peak shaving of DN considering EVs behaviour is implemented using Q-learning based smart charging method. Later the sizes of DGs are determined using EGOA technique. EGOA is an enhancement in the already existing grasshopper optimization technique that improves the algorithm's exploring capability. The hybrid approach helped in identifying the strong buses for EV charging station and DG placement. Further a peak shave of overall DN demand is achieved using Q-learning technique. Later using EGOA the DG sizing along with optimal EV placement helped in reduction of power loss by around 67% compared to base case. The voltage profile of DN is enhanced with the proposed hybrid approach without using compensation devices.

Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network

<span lang="EN-MY">Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.</span>

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

Transmission network congestion control by DESS through interval computation and capacity optimization via hybrid DE‐PSO technique

Transmission congestion is one of major drawbacks of deregulated power system. Congestion gradually violates the physical, operational and policy constraints of the present electricity market. Congestion management in huge power system remains a challenging and tough task, which can be achieved by introducing one or more Distributed Energy Resources (DERs) such as Distributed Generation (DG), electric vehicles, modern micro grid, Energy Storage Systems (ESSs) etc. over congested lines. Consequently, this work proposes the hourly congestion management by optimal allocation of DG in ‘24 hours’ time frame. Further, Transmission Congestion Rent is used to determine the optimal and sub optimal locations for DG, whereas Differential Evolution and Particle Swarm Optimization based hybrid optimization technique is exploited to identify optimal DG size. In this work, solar based DG along with battery based ESS is considered to act as Distributed Energy Storage System ‘DESS. Subsequently, Interval computation method is introduced in this work to consider the intermittency of solar DG. Battery Energy Storage System ‘BESS’ is integrated with DGs due to its excess energy storage capability during off-load period and make it available at peak load conditions, thereby enhancing the overall efficiency of the network. The ‘24 hours’ solar irradiance and temperature data of Delhi, India is taken into account to mathematically model the power generated from Solar DG. The proposed methodology is tested on IEEE 30-bus system and ‘24 hours’ demand data is generated from the original IEEE load data. From the obtained results, it has been established that the hybrid optimization technique results into a better solution as compared to their individual enforcement in managing available generation resources and congestion problem.

Application of mixed discrete student psychology-based optimisation for optimal placement of unity power factor distributed generation and shunt capacitor

ABSTRACT In order to achieve better benefits, distributed generation (DG) sources of optimum size need to be placed at optimum location. A new optimisation algorithm, named as mixed discrete student psychology-based optimisation (MDSPBO) algorithm, is applied to solve the DG optimisation problem with the objective of real power loss minimisation. Using the proposed MDSPBO algorithm, the location and the sizing of DG have been optimised simultaneously. In this work, optimum placement of unity power factor (UPF) DG and shunt capacitor to two different distribution networks (DNs) are studied. Simultaneous placement of UPF-DG and shunt capacitor is also studied for both the DNs. To analyze the performance of the proposed MDSPBO, statistical analysis has been performed. From statistical analysis, it may be established that the MDSPBO may find the optimum solution and the performance of MDSPBO is quite better compared to other considered optimisation algorithms with high-level of significance.

Heuristic DRP‐DG optimization strategy adopted for maximizing total social welfare in the real time Indian utility network

The energy sector is subjected to an increase in power demand year after year. This increase in energy demand will continue due to the need for energy for modern societies. The service providers should face this situation smartly using renewable energy sources to avoid the intermittent nature of these sources and the fluctuations in the demand. Demand response programs (DRP) can play a vital role in increasing the correlation between the loads and the available generation from renewable energy sources (RES). Modern power systems recommend the use of distributed generation (DG) sources to reduce the transmission losses, contingency, and cost of energy and to increase the system reliability, efficiency, and power system loadability. Optimal DR use in the DG can further improve its performance which is the main focus of this paper. Here, a novel DRP-DG strategy is introduced to size and allocate the DG units for minimum congestion in IEEE 33-bus benchmark power system and real Indian power system having 17 buses. The active involvement of the consumers in the DRP facilitates the ultimate objective of maximized total social welfare (TSW) with minimized generation costs. To justify the above multi-objective optimization, two stages of heuristic optimization are introduced in which the rules are fired in the primary stage for determining the optimal size of DG. Implementation of DRP for achieving comprehensive objective function is vindicated in the secondary stage. A real-time Indian utility network is considered and a trade-off output is realized using heuristic fuzzy logic (FL) and genetic algorithm (GA). The results obtained from using the proposed strategy proved its superiority in achieving substantial cost reduction and maximum TSW compared to other soft computing techniques.

Power loss mitigation and voltage profile improvement by optimizing distributed generation

In a developing country, electricity has become the necessity of the growth industries; thus, the distribution system power quality and reliability are crucial. With low carbon initiatives, renewable energy or distributed generation (DG) is a promising source of electricity and leads the complex distribution system. Vital rises in DGs in power grids will significantly impact the system reliability and security, especially in power losses and voltage profiles parameters. This research focuses on an optimization placement and size of DGs in distribution systems to minimize power loss and improve voltage profile using the Modified Lightning Search Algorithm (MLSA). This research has modelled the practical 69-bus radial distribution system. Then MLSA with a weight summation approach is used to identify the suitable location and size for the DGs in the design proposal stage. The optimization objectives are to reduce power losses and improve the voltage profile, especially at the connection point of DGs. Besides that, load profile, DGs constant load and the solar load in distribution system modelled using MATLAB software. The results of the simulation using MLSA indicated that the optimization allocation and sizes of solar DGs applied with current load and load changes can minimize the power losses and improve voltage profile. These results verify the proposed approach’s effectiveness and success in determining the optimal location and sizing of solar DGs to reduce power losses as well as improve voltage profiles.

An efficient capuchin search algorithm for allocating the renewable based biomass distributed generators in radial distribution network

It is essential to keep the stability of electric distribution network especially during abnormal operating conditions, this guarantees service continuity to the customers. Installing small-scale generating units named distributed generators (DGs) can enhance the stability of the grid via minimizing the system losses and improving the network voltage profile. Many approaches, conventional and metaheuristic, have been conducted in installing the DGs in distribution network, however some shortages like slow convergence and stuck in local optima represent obstacles in identifying the appropriate locations and sizes of DGs. Therefore, this paper proposes an efficient methodology of capuchin search algorithm (CapSA) which is presented for the first time to determine the optimum locations, sizes, and power factors of biomass-based DGs (BDGs) in the radial distribution network. The proposed methodology is implemented via adapting the first rows of CapSA population to integer numbers assigned to the BDGs’ locations. The target is minimizing the network active power loss with keeping the power flow, bus voltage, and transmission line in their normal ranges. Two distribution networks of IEEE 33-bus and IEEE 69-bus are analyzed, the CapSA results are compared to chimp optimizer (CO), heap-based optimizer (HBO), seagull optimization algorithm (SOA), differential evolution (DE), and sine cosine algorithm (SCA). Moreover, installing different numbers of BDGs are presented to validate the proposed approach. Furthermore, statistical tests of Friedman and ANOVA are conducted to assess the CapSA performance. In IEEE 33-bus network, the proposed approach minimized the active and reactive power losses to 7.9745 kW and 10.4134 kVAr respectively via installing four BDGs. While for IEEE 69-bus system, the active and reactive losses are minimized to 2.1092 kW and 1.5065 kVAr respectively via the proposed CapSA. The obtained results confirmed the preference of the proposed CapSA in installing the BDGs in radial networks.

A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

Improving the performance of the electric distribution network is essential to meet the needs of the customer and guarantee the service continuity. Installing generators with small sizes known as distributed generators (DGs) can contribute to enhance the network operation by mitigating the network loss and improving the voltage profile. Integrating these generators in inappropriate places can cause serious consequences to the network operation. Therefore, this paper proposes a novel metaheuristic approach of artificial hummingbirdalgorithm (AHA) to identify the best locations and sizes of biomass-based DGs in radial distribution network. The proposed approach has enriched exploration and exploitation phases that enhancing its search capability and avoiding stuck in local optima. The network active power loss and the voltage deviation are selected as the targets to be minimized. Moreover, a new version of AHA is programmed to solve multi-objective problem with the purpose of mitigating both targets. The analysis is conducted on three radial distribution networks of IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. Three scenarios are implemented in each network, the first one is minimizing the active power loss, the second one is mitigating the voltage deviation, and the last one is multi-objective problem. Also, biomass-based DGs with unity, fixed, and optimal power factors are analyzed. Excessive comparison to fractal search algorithm, particle swarm optimizer, genetic algorithm, the whale optimization algorithm, sperm swarm optimization, tunicate swarm algorithm, pathfinder algorithm, seagull optimization algorithm, and sine cosine algorithm, multi-objective water cycle algorithm, multi-objective grey wolf optimizer, and multi-objective sparrow search algorithm is conducted. Moreover, statistical tests of Wilcoxon, Friedman, ANOVA, and Kruskal Wallis are performed to assess the performance of the proposed approach. The gotten results confirmed the preference and competence of the proposed approach in integrating the biomass-based DGs in radial distribution networks.

Optimal WTGUs Placement for Performance Improvement in Electrical Distribution System Using Improved TLBO

The optimal location and size for Distributed Generation (DG) to get maximum advantages and improve the performance of electrical distribution systems (EDS) is a difficult challenge to solve. With EDS performance indices, this research offers a constrained generalized multi-objective performance index (MOPI) objective function. Improved Teaching Learning Based Optimization (ITLBO) is used to solve the proposed objective function by removing the convergence issue of basic Teaching Learning Based Optimization (TLBO). By optimizing the MOPI, the Wind Turbine Generation Unit (WTGU) is examined for single and multiple DG placement and sizing in EDS performance improvement. The ideal approach reduces the burden of EDS consumer loss allocation by minimizing power losses, improving the consumer voltage profile and voltage stability, increasing the line loadability margin (LLM), and increasing the line loadability margin (LLM). The performance test was carried out on a 33-node EDS and used MATLAB software to demonstrate the efficacy of optimal solution.

A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms.