IEEE 33-bus Radial Distribution Network Research Articles

Smart Grid Solutions for Loss Minimization and Voltage Profile Enhancement using Genetic Algorithm-Optimized Capacitor and Solar PV Integration in Radial Feeder

This research addresses the critical challenge of power loss in distribution systems caused by increasing electricity demand. It focuses on reducing power losses and enhancing voltage profiles in a radial distribution system through the optimal placement of capacitors and solar PV. The project employs comprehensive analysis and optimization techniques, including forward and backward sweep algorithms for load flow analysis and Genetic Algorithm (GA) in MATLAB. The performance of the distribution system is evaluated by considering parameters such as power loss and voltage characteristics. Simulation results on an IEEE 33-bus radial distribution network validate significant improvements after various configurations, including the base case, capacitor case, DG case, and combined DG and capacitor case. In each scenario, active power losses are reduced, reactive power loss diminishes, and voltage profile enhancements are observed. The study further validates these results using ETAP software, ensuring consistency and reliability. Notable improvements are also observed in the Thimi Sallaghari 11KV feeder, showcasing potential practical implementations in real feeders in the future. Different case scenarios are explored for the number of capacitors and DGs, providing insights into the optimal combination for enhanced distribution system performance.

Optimizing PV integration: Addressing energy fluctuations through BIPV and rooftop PV synergy

The widespread availability and affordability of photovoltaic (PV) systems are driving the future of demand-side generation towards end-user-based PV plants. Building-integrated PV systems offer an additional source of electrical energy, but their power output depends on external factors like solar insolation, weather conditions, geographical location, and earth’s rotation, causing non-constant energy generation, even in ideal weather conditions. In grid-connected systems, this variability leads to fluctuations in grid demand. Both BIVP and rooftop PV systems are photovoltaic-based, but their installation differences result in distinct energy generation characteristics. To address this, we propose an innovative approach to optimally integrate BIPV and rooftop PV systems by leveraging their contradictory energy generation nature. By employing mathematical and evolutionary algorithms to design an optimal system and develop a multi-objective optimization model, we address practical design issues. The outcome of these single and multi-objective systems helps minimize fluctuations in grid dependency throughout the year. The proposed system is validated on the IEEE-33 bus radial distribution network using the B&R X20CP1586 PLC, confirming its effectiveness in ensuring a stable and reliable grid performance while mitigating energy fluctuation impacts.

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

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.

Improved Golden Jackal Optimization for Optimal Allocation and Scheduling of Wind Turbine and Electric Vehicles Parking Lots in Electrical Distribution Network Using Rosenbrock’s Direct Rotation Strategy

In this paper, a multi-objective allocation and scheduling of wind turbines and electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs of power loss, purchased grid energy, wind energy, PHEV energy, battery degradation cost, and network voltage deviations. Decision variables, such as the site and size of wind turbines and electric parking lots in the distribution system, are found using an improved golden jackal optimization (IGJO) algorithm based on Rosenbrock’s direct rotational (RDR) strategy. The results showed that the IGJO finds the optimal solution with a lower convergence tolerance and a better (lower) objective function value compared to conventional GJO, the artificial electric field algorithm (AEFA), particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results showed that using the proposed method based on the IGJO, the energy loss cost, grid energy cost, and network voltage deviations were reduced by 29.76%, 65.86%, and 18.63%, respectively, compared to the base network. Moreover, the statistical analysis results proved their superiority compared to the conventional GJO, AEFA, PSO, and MRFO algorithms. Moreover, considering vehicles battery degradation costs, the losses cost, grid energy cost, and network voltage deviations have been reduced by 3.28%, 1.07%, and 4.32%, respectively, compared to the case without battery degradation costs. In addition, the results showed that the decrease in electric vehicle availability causes increasing losses for grid energy costs and weakens the network voltage profile, and vice versa.

Optimal DG Integration Using Artificial Ecosystem-Based Optimization (AEO) Algorithm

This paper presents a novel and efficient optimization approach based on the Artificial Ecosystem Optimization (AEO) algorithm to solve the problem of finding optimal location and sizing of Distributed Generation (DGs) in radial distribution systems. The objective is to satisfy a fluctuating demand in a constant and instantaneous way while respecting the requirements of power loss reduction, operating cost minimization and voltage profile improvement within the equality and inequality constraints. The robustness of the proposed technique in terms of solution quality and convergence characteristics is evaluated using the IEEE-33 bus radial distribution network test system. The simulation results are compared with those of other methods recently used in the literature for the same test system. The experimental outcomes show that the proposed AEO approach is comparatively able to achieve a higher quality solution within a timeliness of computation.

Integration of Solar Photovoltaic Distributed Generators in Distribution Networks Based on Site’s Condition

The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature.

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes, and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. Consequently, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems, and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence, and shorter running time.

Development of DSTATCOM Optimal Sizing and Location Technique Based on IA-GA for Power Loss Reduction and Voltage Profile Enhancement in an RDN

The efficiency of electrical distribution systems is being more affected by the increase in voltage drops and power losses. These issues of voltage drop and power loss can be significantly minimized by the incorporation of a Distribution Static Compensator (DSTATCOM) in the distribution network. However, inappropriate positioning and sizing of DSTATCOM can undermine its efficiency. Despite the contributions of many researchers to the optimal placement of DSTATCOM and other compensators in distribution networks, the problems of voltage drop, power losses, and power quality persist, necessitating the need for additional research in this area. In this paper, an innovative technique based on hybridized Immune and Genetic Algorithm (IA-GA) for optimal DSTATCOM placement and sizing for three distinct load levels is proposed. Simulation and analysis of the proposed algorithm were carried out using IEEE-33 bus radial distribution network (RDN) in MATLAB. The simulation results demonstrate a substantial decrease in power loss and a significant improvement in the voltage profile. Evaluation of the proposed method against existing techniques reveals that the proposed technique outperforms IA and PSO in terms of decreasing power loss and enhancement of voltage profiles. A cost-benefit analysis was performed, and it was discovered that the proposed technique yields improved annual cost savings.

Analysing the impact of optimally allocated solar PV-based DG in harmonics polluted distribution network

The optimal installation of a renewable distributed generation (DG) in a radial distribution network (RDN) provides a sustainable and clean solution for the future smart network. A weighted-sum multi-objective approach is applied to determine the optimal location of solar PV-DG followed by a PSO algorithm to find optimal DG size. The impact of optimal DG allocation in distorted and non-distorted distribution network is efficaciously demonstrated and validated on IEEE 33-bus RDN. The harmonic analysis in the presence of the six-pulse converter non-linear load and non-linear DG is carried out for IEEE 34-bus network. The non-linear loads are modelled as harmonic current source. As composite load model provides lowest voltage drop with decreased capital investment cost, hence, its effect and the effect of constant power load model on energy loss saving, network loss and total harmonic distortion at various load levels is being analysed. The harmonic load flow results obtained considering non-linear and linear loads are compared to other analytical methods and nature-inspired techniques to illustrate the robustness of the proposed strategy for enhancing the performance of the network. The statistical significance of the results is verified by the Wilcoxon test.

A robust SMES control for enhancing stability of distribution systems fed from intermittent wind power generation

The voltage and frequency stability issues of power systems are the main challenges that arise from high penetration levels of wind energy systems. This paper presents an effective solution for voltage and frequency stability problems by using a superconducting magnetic energy storage (SMES) system controlled with a fuzzy logic controller (FLC). The proposed control system can suppress the voltage and frequency fluctuations due to the high variations of wind speed. In addition, the proposed control system is suitable for both balanced and unbalanced distribution systems with high penetration levels of wind turbines (WTs). A squirrel cage induction generator (SCIG) is selected in the case study, which represents the worst scenario of WT generation from the voltage and frequency stability aspects. This is due to the high reactive power consumed by the SCIG from the utility grid during steady state and voltage fluctuations that may lead to harmful consequences for power system components. The proposed control method is validated using the IEEE 33-bus radial distribution network (RDN), wherein the SMES and WT systems are connected to the weakest points of the RDN. The obtained results demonstrate the superior performance of the proposed FLC-SMES system for alleviating the voltage and frequency fluctuations of the distribution power system during high variations of wind power.

Optimal sizing and location of distributed generation for loss minimization using firefly algorithm

<span>Distributed generation (DG) plays an important role in improving power quality as well as system realibility. As the incorporation of DG in the power distribution network creates several problems to the network operators, locating a suitable capacity and placement for DG will essentially help to improve the quality of power delivery to the end users. This paper presents the simulation of an application of firefly algorithm (FA) for optimally locating the most suitable placement and capacity of distributed generation (DG) in IEEE 33-bus radial distribution network. This strategy aims at minimizing losses together with improving the voltage profile in distribution network. The losses in real power and voltages at each bus are obtained using load flow analysis which was performed on an IEEE 33-bus radial distribution network using forward sweep method. The proposed method comprises of simulation of the test system with DG as well as in the absence of DG in the system. </span><span>A comparison between the Firefly Algorithm (FA) with Genetic Algorithm (GA) is also demonstrated in this paper. The results obtained have proven that the Firefly Algorithm has a better capability at improving both the voltage profile and the power losses in the system.</span>

Coordinated control for voltage regulation of distribution network voltage regulation by distributed energy storage systems

With more and more distributed photovoltaic (PV) plants access to the distribution system, whose structure is changing and becoming an active network. The traditional methods of voltage regulation may hardly adapt to this new situation. To address this problem, this paper presents a coordinated control method of distributed energy storage systems (DESSs) for voltage regulation in a distribution network. The influence of the voltage caused by the PV plant is analyzed in a simple distribution feeder at first. The voltage regulation areas corresponding to DESSs are divided by calculating and comparing the voltage sensitivity matrix. Then, a coordinated voltage control strategy is proposed for the DESSs. Finally, the simulation results of the IEEE 33-bus radial distribution network verify the effectiveness of the proposed coordinated control method.

Mobile Power Infrastructure Planning and Operational Management for Smart City Applications

The paper presents new strategies and algorithms for future mobile power infrastructure planning and operational management in smart cities. The efforts have been made to develop a resilient Electric Vehicle (EV) infrastructure for smart city applications. The goal of this work is to maximize the profit of utility and EV owners participating in real-time smart city energy market subjected to numerous techno-economic constraints of the EVs and power distribution system. For effective real-time applications, the knowledge of artificial intelligence and internet of things (IoT) are used in the proposed model. In order to validate the proposed model for smart city applications, IEEE 33-bus radial distribution network is adopted as a small city power network. The simulation results of proposed model are found to be encouraging when it is compared with the case in which conventional strategies are used.

Influence of phasor adjustment of harmonic sources on the allowable penetration level of distributed generation

Harmonic distortion caused by increasing size of inverter-based distributed generation (DG) can give rise to power quality problems in distribution power networks. Therefore, it is very important to determine allowable DG penetration level by considering the harmonic related problems. In this study, an optimization methodology is proposed for maximizing the penetration level of DG while minimizing harmonic distortions considering different load profiles. The methodology is based on updating the voltage magnitude and angle at point of common coupling depending on the size of DG to be utilized in the harmonic power flow modeling. The harmonic parameters are determined by using decoupled harmonic power flow method, in which the harmonic source modeling with harmonic current spectrum angle adjustment is embedded, while the nonlinear loads and inverter-based DGs are connected to the distribution power network. The allowable penetration level of DGs is determined based on power quality constraints including total harmonic voltage distortion, individual harmonic voltage distortion, and RMS bus voltage limits in the optimization framework. Fuzzy-c means clustering method is also applied to decrease the computational effort of the optimization process in the long-term load profile. The effectiveness of the proposed method is illustrated on the IEEE 33-bus radial distribution network for different scenarios.

Adaptive Zone Identification for Voltage Level Control in Distribution Networks With DG

A decentralized voltage control is proposed for distributed generation (DG) units to provide short and long-term voltage support in distribution networks. Local controllable zones are used to determine the voltage control boundaries for each DG unit. The number of zones and their size depend on the number, location and size of the DG units, and can be reconfigured in real time in response to network topology changes. The performance and value of the proposed control approach are demonstrated under various operating scenarios. The study is based on the IEEE 33-bus radial distribution network implemented in DIgSILENT PowerFactory.