IEEE 33-bus Radial Distribution Research Articles

Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response

In the energy management of a network, it is expected that by extracting the optimal performance for the power sources, storage equipment, and responsive demand, a favorable economic and technology situation is achievable for the network and the mentioned elements. Virtual power plants, as a unit aggregating resources, storage, and responsive loads, can create more favorable conditions in network energy management. So, it is expected that the positive effect of the virtual power plant format on the economic and technical situation of the distribution system is far more than those of managing individual elements mentioned in the network. Consequently, the distribution network operator's economic, environmental, and technical goals are met through the concurrent administration of reactive and active power in the smart distribution network that is equipped with a flexible-sustainable virtual power plant. The system operator is accountable for reducing the weighted sum of the voltage security index, energy loss, and energy cost of the distribution network. This problem is associated with the optimal power flow formulation, which considers the environmental limits and security of voltage in the distribution network, the renewable resource operation model and flexibility in the form of a virtual power plant, and the system's flexibility constraints. Flexibility resources considered in the present study are price-based demand response and electric vehicle parking lots. Stochastic optimization relying on the Unscented Transform assists in providing a suitable model for uncertain quantities resulting from the amount of load, electric vehicles, renewable power, and price of energy and eventually shortens the computing time and accurately computes the flexibility index. The optimal compromise solution amongst various objective functions can be found through fuzzy decision-making. Some innovations of this research include concurrent administration of active and reactive power in virtual power plant, concurrent modeling of economic, operational, environmental, voltage security, and flexibility indicators in the distribution network, utilization of electric vehicles, and demand response as a source of flexibility, use of Unscented transform for modeling the uncertainties corresponding to the exact calculation of flexibility. The suggested method was simulated in the IEEE 69-bus radial smart distribution system. Regarding the numerical report obtained, the optimal performance of each of the renewable generation, demand response, and parking of electric vehicles can significantly impact the economic and technical condition of the distribution network. However, the best condition was obtained when the mentioned elements were placed in the form of a virtual power plant. So, in such a situation, the energy cost is around $1862 for the said network. The lowest value for the worst security index in this network is around 0.933 p.u. Energy loss, maximum voltage drop, and peak load carrying capability are equal to 1.902 MWh, 0.047 p.u., and 5.624 MW, respectively. As a result, and based on numerical findings, the method can attain sustainable social welfare. The optimal power scheduling of sustainable systems can enhance the economic, security of voltage, and operational, conditions of the network by roughly 43 %, 26.9 %, and 47 %-62 %, respectively, compared to power flow studies. Furthermore, the ideal administration of virtual power plants enables the proposed plan to achieve 100 % flexibility. Additionally, it can substantially diminish the degree of contamination within the distribution network.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

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.

Optimal Placement and sizing of DG Units Using LCA Algorithm

The motivation of this paper is to present an approach that relies on the league championship algorithm (LCA) to determine the most effective number, placement, and size of distributed generations (DG) within distribution systems. The primary objective of this approach is to minimize the losses of the power system, as well as to enhance the voltage profiles and stability index of the voltage. The optimal location of the DG units is determined through the use of the Loss Sensitivity Factor (LSF), while the optimal size of the DG units is found through the use of LCA. The IEEE 33-bus and 69-bus radial distribution systems were both tested to validate the proposed method, and the results obtained from LCA were compared to those of other methods found in the literature. The simulated results have shown that the LCA method proposed in this paper is highly effective and performs exceptionally well when addressing the problem of optimal location and sizing of DG units in radial networks.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Optimal allocation of open unified power quality conditioner in integrated distribution system environment with renewable energy sources

ABSTRACT The growing utilization of renewable energy sources (RES) that rely on inverters has adverse impacts on distribution systems, resulting in challenges such as power quality (PQ) issues and increased losses in the distribution network. One potential remedy for these issues involves the strategic allocation of open unified power quality conditioner (UPQC-O). This paper introduces a comprehensive two-layer integrated optimization model designed for the allocation of UPQC-O and RES in an active distribution network. The primary objective is to enhance energy efficiency, PQ, and economic benefits simultaneously. Furthermore, an innovative solver known as the multi-improved whale optimization method (multi-IWOA) has been implemented to address the optimization problem at hand. The efficacy of this approach was validated through testing on both the standard IEEE 69-bus radial distribution system and a real-world Indian 52-bus system. The evaluation results underscore the significance of optimally distributing UPQC-O resources while taking into consideration the integration of RES within the distribution system. It’s worth noting that the proposed methodology achieved a substantial 56.72% cost reduction for the IEEE 69-bus radial distribution system and a significant 76.092% cost reduction for the Practical System Indian 52-bus system. This highlights the substantial advantages of the proposed methodology for the efficient management of UPQC-O and RES integration in distribution systems.

Enhancing voltage stability and Minimizing Power Losses via Reconfiguration and Sizing of Distributed Generators

This paper provides an alternative approach for improving voltage stability and minimizing power losses through distribution system reconfiguration and adjusting the capacities of the Distributed Generators (DGs). The problem is formulated as multi-objective functions. Multi-objective genetic algorithm is utilized to evaluate the highest system loadability and minimum power losses by optimally sizing the distributed generators along with optimal network reconfiguration. The proposed technique is applied to IEEE 69-bus radial distribution system. The results showed the effectiveness of the proposed approach in improving the voltage profile and reducing in the power losses of the system.

Enhancing the performance of radial distribution systems via optimal integration of electric vehicles

<span lang="EN-US">Electric vehicles (EVs) and their associated charging stations (CSs) are a promising avenue for greenhouse gas reduction and energy security. However, improper location and size of the EV charging station (CSs/EVs) negatively affect the power system. It results in some technical challenges such as increased real power-loss, decreased voltage stability and increased voltage deviation. This paper suggests optimum planning of CSs/EVs locations to conserve voltage, improve power-loss and reduce the effect of CSs/</span><span lang="EN-US">EVs in electrical distribution systems. The multi objective genetic algorithm method (MOGA) is utilized to solve the optimization problem and reduce the impact of the random location of CSs/EVs. <span>The simulations are</span> performed on IEEE 33-bus and IEEE 69-bus radial distribution systems (RDS). In addition, the optimal EV allocation is carried out with two fixed levels of loading from 100% to 150% of the candidate EV load. A comparison between the proposed MOGA technique and other optimization techniques is carried out. The results demonstrated the capability of the proposed technique for optimal placement of CSs/EVs in RDS. </span><span lang="EN-US">Moreover, it is providing an objective value to solve a complex multi-objective nonlinear optimization problem to reduce the total power-loss, improve the voltage profile and extend the voltage stability.</span>

Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems

This paper proposes a novel placement technique based on the improved golden jackal optimization (IGJO) algorithm for multiple capacitor banks (CBs) and multi-type DGs in a distribution network considering single and multi-objective problems. The proposed algorithm incorporates memory-based equations and random walk strategy to enhance the performance of the recent golden jackal optimization in terms of accuracy and convergence speed. The optimization problem is formulated as a weighted multi-objective that seeks to enhance the voltage profiles, boost stability, and minimize the total active power loss. An index named reactive loss sensitivity (QLSI) is also employed with the developed IGJO to identify the candidate nodes for the DGs and CBs installation to reduce the search space of the optimization algorithm. The robustness of the developed IGJO algorithm is evaluated through the CEC 2020 benchmark functions, and a comparison study is conducted with the original GJO and the other nine fresh competitors using various statistical tests to confirm its dominance and superiority. Then, the proposed IGJO is implemented in single and multi-objectives for the optimal deployment of multiple CBs individually and simultaneously with multiple DGs with different operating modes to enhance the performance of the IEEE 69-bus radial distribution system (RDS). The fetched outcomes are compared with the original GJO, weevil optimizer algorithm (WeevilOA), skill optimization algorithm (SOA), and Tasmanian devil optimization (TDO) to further measure its efficacy using different statistical tests. The IGJO algorithm is also applied to deploy multiple DGs for the IEEE 118-bus RDS with the aim of minimizing active loss. The simulation findings affirmed that the proposed IGJO technique beats the other rivals in all investigated situations, qualifying for the optimal inclusion of DGs in the presence of generation and demand uncertainties. Specifically, the integration of three units of CBs synchronously with three DGs Type-I and DG Type-III reduces the active power loss to 4.2664 kW and 3.4178 kW, respectively. The lowest power loss, approximately 2.7989 kW, is achieved with the simultaneous integration of three DG Type-I and DG Type-III using the developed IGJO algorithm.

IEEE-69 Distribution Network Performance Improvement by Simultaneously Optimal Distributed Generation Sizing and Location Using PSO Algorithm

Integrated distributed generation (DG) to the network is the most popular technique to improve the network performance since DG sizing and location could reduce the power loss. However, using a non-optimal DG sizing and location could cause voltage fluctuation and power loss increase. Therefore, it is very important to develop a suitable optimization method that determines the optimal DG sizing and location. This research proposes a new strategy that leads to finding the simultaneous optimal solution involving both the optimal DG sizing and location. The proposed method aims to reduce the overall fitness function that taking into account both the voltage profile index and the power loss reduction. The particle swarm optimization (PSO) technique is the method that is used in this work. MATLAB simulations carried out on IEEE 69-bus radial distribution networks were used to prove the ability of the suggested method. The results obtained demonstrate the effectiveness of the presented method to determine the simultaneously optimal sizing and location of the DG units that minimize the overall power loss and improve the overall voltage profile.

Exploration and mitigation of power quality problems in radial distribution system by placing distributed generation through voltage stability index

Introduction. Distributed generation has played an important role in many aspects of sustainability, such as improving voltage profiles and reducing power losses, in the distribution network. Problem. Frequent variation of loads causes many complications while placing and sizing of distributed generation in the radial distribution network, via quality of supply, and stability of the system. Goal of the paper is to investigate and mitigate the power quality issues towards stabilizing the system during distributed generations placed in the system under various loading conditions. Methodology. The line voltage stability index analyses and enhances the performance of the radial distribution network by effective sizing and location of distributed generation towards the objective function. Practical value. A standard test system IEEE-69 bus radial distribution network is used to understand through MATLAB environment.

Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Reduction of Emission Cost, Loss Cost and Energy Purchase Cost for Distribution Systems With Capacitors, Photovoltaic Distributed Generators, and Harmonics

In this paper, a bonobo optimizer (BO) and two other methods, particle swarm optimization (PSO) and salp swarm algorithm (SSA), are implemented to determine the location and sizing of photovoltaic distributed generators (PDGs) and capacitors in IEEE 69-bus radial distribution system with many nonlinear loads. The objective of the study is to minimize the costs for purchasing energy from main grid for load demand and power loss on transmission lines as well as cost for emission fines from fossil fuel generation units of the grid under considering strict constraints on penetration, voltage, current and harmonic distortions. The results have shown that BO is the best and most stable method in solving the considered optimization problem. With the use of the optimal solution from BO, the total cost is significantly reduced up to 80.52%. As compared to base system without capacitors and PDGs, the obtained solution can reduce power loss up to 94.48% and increase the voltage profile from the range of [0.9092 1.00] pu to higher range of [0.9907 1.0084] pu. In addition, total harmonic distortion (THD) and individual harmonic distortion (IHD) are also much improved and satisfied under the IEEE Std. 519. Thus, BO is a suitable method for the application of installing capacitors and PDGs in distribution systems.

Optimal size and location of distributed generations in distribution networks using bald eagle search algorithm

Introduction. In the actual era, the integration of decentralized generation in radial distribution networks is becoming important for the reasons of their environmental and economic benefits. Purpose. This paper investigate the optimal size, location and kind of decentralized generation connected in radial distribution networks using a new optimization algorithm namely bald eagle search. Methods. The authors check the optimal allocation of two kinds of decentralized generation the first is operated at unity power factor and the second is operated at 0.95 power factor, a multi-objective functions are minimized based on reduction of voltage deviation index, active and reactive power losses, while taking into consideration several constraints. Results. Simulation results obtained on Standard IEEE-33 bus and IEEE-69 bus radial distribution networks demonstrate the performance and the efficiency of bald eagle search compared with the algorithms existing in literature and radial distribution networks performances are improved in terms of voltage profile and notably active and reactive power losses reduction, decentralized generation operated at 0.95 power factor are more perfect than those operated at unit power factor.

A multi-objective equilibrium optimization for optimal allocation of batteries in distribution systems with lifetime maximization

Many economic, environmental, and technical benefits have been achieved in recent years as a result of the integration of renewable energy resources (RERs) and battery energy storage units (BESUs) through the distribution network. However, unplanned allocating of the integrated BESUs and RERs leads to excessive investment costs and insecure system operation. Moreover, the charging and discharging processes have a direct impact on the degradation of the BESUs, and consequently, the stability of the BESUs will be affected as the battery ages. Therefore, this paper presents the trade-off between different conflicting objectives. In the presented multi-objective model, the battery lifetime maximization is considered in the allocation problem, for the first time, by maximizing the battery cycle to failure (CTF). On the other hand, different beneficial objectives are included by minimizing the operation and investment costs of the integrated units, the cost of energy not supplied (CENS), the cost of power loss, and the cost of carbon dioxide (CO2) emissions. For solving this model, a multi-objective equilibrium optimization technique (MOEOT) is proposed to determine the optimum sites and sizes of photovoltaic (PV) and BESUs, maximum and minimum battery state of charge, and the charge hours. The proposed approach is employed on IEEE standard 30-bus and 69-bus radial distribution networks. The proposed MOEOT successfully acquires Pareto optimal front with diverse candidate options of different CTF and corresponding costs. Also, the impacts of varying depth of discharge (DOD) and charge and discharge hours for BESUs on the system performance are analyzed. For the IEEE 30 bus system, as the hours of the battery charge and discharge are increased from 2 to 12 h, the battery CTF is increased by 1 %; the power losses costs are decreased by 8.6 %; the emission costs are decreased with 1 %, and the life cycle cost is decreased with percentage 1 %. For the IEEE 69 bus system, the battery CTF is increased by 1 %; the power loss costs are decreased by 17 %; the emission costs are decreased by 1.8 %, and the life cycle cost is decreased by 0.1 %.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

A Novel Technique for Optimal siting and Rating of Shunt Capacitors Placed to the Radial Distribution Systems

The overall efficacy of power distribution is reduced through power loss in distribution systems owing to branch resistance. For providing quality power to customers, power losses must be diminished, and voltages must be enhanced. This paper introduces a novel improvement for the traditional Salps Swarm Algorithm (SSA) called Enhance SSA (ESSA) for selecting Optimal Shunt Capacitor Ratings and their Locations (OSCRL). ESSA is viewed as an enhanced version of SSA with the aim of improving the SSA's poor performance. A multi-objective function was designed in this work for reducing real power loss and Total System Cost (TSC), as well as, improving feeder voltage profile and maximizing Net Savings (NSs) per year under security restrictions. For validating and evaluating the proposed ESSA technique, a practical 33-bus local Iraqi and standard IEEE 69-bus Radial Distribution Systems (RDSs) were used as test systems through utilizing MATLAB. The experimental results attained by the proposed ESSA are compared with other approaches currently available in the literature for addressing OSCRL problem. The experimental results prove the efficacy and supremacy of the ESSA to other literature algorithms with respect to diminishing power loss as well as TSC, reinforce bus voltage profile and increase NSs per year for multiple RDSs.

An improved MOPSO algorithm for optimal sizing & placement of distributed generation: A case study of the Tunisian offshore distribution network (ASHTART)

Investigation of power distribution systems is an attractive field in the power system analysis. The location and size of Distributed Generation (DG) are important and an uncontrollable choice might result in a negative impact on the system’s behavior. This paper presents an improved approach for optimum simultaneous DG placement and sizing to reduce power loss and improve the voltage profile and the stability of distribution networks. Numerous methods have been used for solving this difficult combinatorial problem like Particle Swarm Optimization (PSO) algorithm used in this work. The suggested MOPSO method combined with MATPOWER toolbox was tested on standard IEEE 33-bus and IEEE 69-bus radial distribution networks (RDN) throw different scenarios. The major contribution of this paper was that the procedure was applied to the Tunisian electricity distribution network (ASHTART) of the SEREPT (Society for Research and Exploitation of Petroleum) to prove the efficiency of applying this method on real-world systems. This paper will also compare the proposed method with previous findings methods and the obtained results testify to the robustness and effectiveness of the improved MOPSO algorithm in terms of reduction in total active power losses (TAPL), maximization of a percentage of minimum voltage improvement (%MVI), amelioration in voltage profiles and also maximization in voltage stability index (VSI) while finding the optimal location and size of the DG units (OPSDG).

Strategic integration of battery energy storage and photovoltaic at low voltage level considering multiobjective cost-benefit

Renewable energy sources, such as solar photovoltaic (PV) systems and battery energy storage systems (BESS), help reduce greenhouse gas emissions while fulfilling the world?s growing energy demand. The inclusion of BESS reduces the peak hour demand, and control of charging and discharging of BESS can be economical for distributors facing time-based energy pricing. This paper discusses a novel multiobjective Horse herd optimisation algorithm (MOHHOA) approach, which is inspired by the social behaviour of horses in herds for PV and BESS optimal allocation in the radial distribution system. The proposed algorithm combines multiple benefits like benefits from economic gain per day, reduction in CO2 emission per day, and reduction in energy loss per day. IEEE 69-bus radial distribution system (RDS) is used for testing the suggested approach. The case studies and simulation results show that the suggested model effectively accommodated PV power generation in IEEE 69-bus RDS without violating any system constraints. Results indicate improvement in node voltage profiles, security margins and energy losses, and peak energy savings, and system characteristics as a whole, and there were significantly improved techno-economic performance of the distribution system.

Whale Optimization Algorithm-Based DG Allotment for Loss Minimization of Distribution Networks

For optimum placement of distributed generation (DG) units in balanced radial distribution network for loss minimization, implementation of whale optimization algorithm (WOA), a state-of-the-art meta-heuristic optimization algorithm is proposed in this paper. Encouraged by bubble-net hunting strategy of whales, WOA mimes the collective practice of humpback whales. For validating performance in solving the mentioned problem, the suggested technique is implemented on IEEE 33-bus and IEEE 69-bus balanced radial distribution test networks. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used as a propitious substitute in practical power systems to overcome the optimum DG siting and sizing issue. Also concerning the best knowledge of the authors, it is the first report on the application of WOA in solving optimum DG siting and sizing issue.