Active Power Loss Reduction Research Articles

Optimal Location and Sizing of a D-STATCOM in Electrical Distribution Systems to Improve the Voltage Profile Considering the Restriction of Harmonic Injection through the JAYA Algorithm

This study focuses on the application of the JAYA algorithm to optimize the implementation and sizing of a distribution static synchronous compensator (DSTATCOM) in distribution systems to reduce power losses and enhance voltage profiles, ensuring a total harmonic distortion of voltage (THDv) below 3% at all system nodes. The algorithm, developed and modelled in MATLAB, addresses power flow solutions and analyzes harmonic influence from implementing a DSTATCOM as reactive compensation via a non-iterative harmonic penetration analysis. Successful algorithm implementation results in a significant reduction in both active and reactive power losses in 33- and 34-node systems while maintaining a THDv below 3% at all nodes. Although imposing the THDv limit constraint reduces power loss, this compensation ensures low THDv levels in the voltage. In contrast to existing literature that focuses on power loss reduction via reactive compensation, this work addresses and controls the inclusion of harmonics in the electrical network as a consequence of such reactive compensation, marking a novel contribution to the field.

Multi-objective Adaptive Fuzzy Campus Placement based Optimization Algorithm for optimal integration of DERs and DSTATCOMs

The novel Adaptive Fuzzy Campus Placement based Optimization Algorithm (AFCPOA) presented in this paper is expected to solve the issue of single objective and multi objective optimal optimization problems. AFCPOA’s performance was evaluated on 25 benchmark optimization test functions and compared to other existing methods. Optimal location and sizing of Distributed Generation (DG), Distribution STATtic COMpensator (DSTATCOM), DGs and DSTATCOMs, Battery Energy Storage Systems (BESS) in the presence of PVDGs, and BESS in the presence of PVDGs and DSTATCOMs were integrated into Radial Distribution System (RDS) to fulfil the objectives. Technical advantages of DG include active power losses reduction and control of voltage dips. DSTATCOM functions as a capacitor, injecting reactive power to improve the consistency and quality of power flow and sustain voltage against any imbalance and distortion on load side or supply side. The appropriate location and capacity for BESS present a significant barrier for the integration of BESS and renewable DGs with distribution networks. The objective functions in this problem are to minimize total real power losses, reactive power losses, and voltage deviation. Additionally, the improvement of voltage profile was taken into consideration as a constraint while choosing the best location. Finally, the suggested algorithm is evaluated on IEEE 33 bus RDS and PG & E 69 bus RDS. Simulation results show the proposed method’s excellent performance and applicability.

An Investigation of the Impact of Distributed Generation Penetration on Directional Overcurrent Relay Coordination in a Distribution Network

Distributed generation units (DGs) are rapidly becoming widespread in distribution systems due to their advantages such as power loss reduction, voltage profile improvement, and economic returns. Many researchers seek new ways to maximize their these advantages. However, their impact on the fault current is a problem for the field of power system protection. The changes in the short-circuit currents due to DGs cause the miscoordination of the directional overcurrent relays (DOCRs). In this paper, the impact of distribution generation penetration on DOCR coordination is analyzed and investigated. Besides this negative impact of DGs, their contributions to reducing power loss and improving the voltage profile are also analyzed for different DG penetration levels. The gazelle optimization algorithm is utilized to solve the DOCR coordination problem studied in this paper. The method is performed on the distribution section of the IEEE 14-bus system. It is seen that a significant number of miscoordinations occur when even the DG penetration is increased by about 10%. With the increase in DG penetration, the number of miscoordinations does not increase proportionally, but there is a proportional increase in active and reactive power loss reduction and voltage profile improvement.

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.

Minimization of Active Power Loss Using Enhanced Particle Swarm Optimization

Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system’s power loss. The line stability index (Lmn) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a prominent solution. EPSO and other PSO variants minimized the system’s actual/real losses. Nodes 8 and 14 were identified as the critical nodes of the IEEE 9 and 14 bus systems, respectively. The power loss of the IEEE 9 bus system was reduced from 9.842 MW to 7.543 MW, and for the IEEE 14 bus system, the loss was reduced from 13.775 MW of the base case to 12.253 MW for EPSO. EPSO gives a better active power loss reduction and improves the node’s voltage profile than other PSO variants and algorithms in the literature. This suggests the feasibility and suitability of EPSO to improve the grid voltage quality.

Technique for Reactive Loss Reduction and Loading Margin Enhancement Using the Curves of Losses versus Voltage Magnitude

This paper presents an alternative methodology to obtain the precise amount of shunt reactive power compensation in order to simultaneously reduce the total reactive power losses, improve the voltage profile and increase the loading margin of power systems. The amount of shunt reactive power compensation to be allocated is determined based on the curve of the total reactive power losses versus the voltage magnitude of a chosen bus. The best places for shunt reactive compensation are defined by the load bus participation factors of the critical mode provided by the static modal analysis. Simulation results employing shunt capacitors obtained with the new approach for the IEEE test systems (14, 57 and 300 buses) show that the procedure leads to a reduction in total reactive and active power losses and simultaneously improves the voltage profile and loading margin.

Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

Solution to the deterministic and stochastic Optimal Reactive Power Dispatch by integration of solar, wind-hydro powers using Modified Artificial Hummingbird Algorithm

In electrical power networks, the optimal reactive power dispatch (ORPD) problem is essential to the system studies to perform reliable and secure operations by maintaining the control variable within their permissible limits. An electric network consisting of thermal generators has been studied widely for optimal power dispatch problems. Increasing renewable energy resources (RERs) penetration into the electric power grid required power flow studies while integrating these resources. It is a strenuous task to incorporate renewable energy resources into the ORPD problem due to the stochastic nature of RERs. This paper solved the stochastic optimal reactive power dispatch (ORPD) problem considering the uncertainties of renewable resources such as; solar PV, wind turbine, and hydropower generation systems. The time-varying load demand and the power generated from the renewable energy resources are represented using the normal, the lognormal, the Weibull, and the Gumbel probability density function (PDFs). Then, the Monte Carlo simulations reduction of scenarios-based technique is applied to generate a suitable number of scenarios. The second contribution presents an efficient version of the Artificial Hummingbird Algorithm (MAHA) for solving the Stochastic and Non-Stochastic ORPD. The proposed MAHA is based on the levy flight motion and the distance bandwidth motion around the best solution to enhance the exploration and exploitation behavior of the traditional AHA to avoid trapping into the local minima. The proposed algorithm is validated and tested on the IEEE 30-bus system for active power loss reduction, voltage profile improvement, and voltage stability enhancement. The results showed the effectiveness and superiority of the proposed MAHA for solving the ORPD problem compared to the well-known conventional algorithms such as AHA, GWO, SCA, DO, BWO and other state-of-the-art algorithms.

Multi‐objective Optimization of Optimal Placement and Sizing of Distributed Generators in Distribution Networks

Due to the growing attention for the environmental impacts and power loss minimization, distributed generators (DGs) have been introduced widely into the electric power system. One of the challenges of integrating with the power system is to determine their optimal placement and sizing, which, when not respected, adversely affects the performance of the electrical network. In this paper, three multi‐objective algorithms of particle swarm optimization (PSO), variable constants (VCPSO) and genetic algorithm (GA) are adopted and implemented. The main objectives are to detect the optimum size and location of multiple DGs aiming to reduce the active power loss and improve bus voltage deviations in the distribution networks. The paper conducts a comprehensive review of the optimal size and location of the DG via systematic procedures, including definition, classifications, technologies of DGs. Then, the performances evaluation of the three optimization methods are presented and compared with other methods. The presented optimization methods are tested on the IEEE‐33 bus, 32‐line radial distribution network. Four different scenario‐based studies including base case and different number of DGs are performed to examine the accuracy of the presented algorithms. The obtained results prove that all the three algorithms are suitable for this multi‐objective optimization and VCPSO offers the best solution in terms of convergence and, and it has lowest average computation time. The performance and accuracy of the presented approaches and their improvements in the power loss and bus voltage profile are discussed and presented in detail. The obtained results show that more than 65% of active power loss reduction has been attained with the proper sizing and placement of DG systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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.

Search group algorithm for optimal allocation of battery energy storage with renewable sources in an unbalanced distribution system

ABSTRACT In recent decades, numerous distributed energy resources (DER) have been linked to distribution networks. Among these wind turbine generators (WTG) are popular for their uncertain power fluctuations. Using battery-based energy storage devices (BESS) as a solution to these unwanted fluctuations might be a realistic option. However, the integration of BESS with WTG on a wider scale requires additional attention to the size, position, and charge-discharge schedules. This article discusses these perspectives in addition to the location and power penetration levels of WTGs. In addition, a new charge-discharge control model is created for deciding when batteries should be charged or discharged at each hour. A multi-objective optimization problem is created with the goal of optimizing the economic, environmental, and technological goals of the unbalanced distribution network (UDN). The optimization task is solved using a Search Group Algorithm (SGA). The planning model leverages the average demand of the feeder at a particular hour of the day as the decision criteria for charging or discharging the battery. The charging of the BESS devices is totally done by the WTG power output. Furthermore, the conservative and free-running discharge approaches of the battery are studied. The proposed approach is tested using IEEE 37 bus UDN and the results demonstrate improvement in distribution system performance by coordinating BESS dispatches appropriately. The active and reactive power loss reduction for the three-phase system were 30.4% and 30.8% for the free-run mode, respectively, while the same for the conservative mode was 40.3% and 37.4%.

DEVELOPMENT OF ANN CONTROLLED UPFC BASED PROTECTION MODEL FOR NIGERIAN 330KV POWER LINES

This paper, proposed the use of artificial neural network (ANN) based UPFC for transmission network loss minimization. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. Power loss leads to high cost of power generation, transmission and distribution. Unlike exiting up change and regulating transformer techniques for loss reduction, FACTS devices have fast switching capability and can be subjected to very free control algorithms for more optimal performance in loss reduction application in power systems. The neural network was modeled to output the firing angle to enable the FACTS device effectively control the adsorption and injection of reactive power for transmission loss reduction. The Nigerian 330KV power grid was used as a case study for the evaluation of the proposed power loss reduction system A digital model of the case study power system with the proposed neural network controlled UPFC integrated was created in the MATLAB/SIMULINK programming environment. Genetic algorithm was used for the optimal placement of the FACTS device in the MATLAB/SIMULINK model of the Nigerian 330KV transmission system. Simulation carried out involved alteration of power flow in order to cause different levels and distribution of losses in the network. With each variation, load flows was carried out to evaluate the distribution of losses and the active and reactive loss reduction achieved by the proposed system. The simulation and evaluation were carried out under two scenarios: (i) with the UPFC installed and (ii) without the UPFC installed. With each variation of the load at the bus, load flow is run to determine total system loss either with the UPFC installed or without the UPFC installed. Results obtained showed that the proposed system achieved an average active power loss reduction of 12.8416% and an average reactive power loss reduction of 21.82%.

Improving efficiency in a distribution network with asymmetric load due to connected solar panels with a phase relationship

In the article, the distribution of symmetrical loads operating in extreme load, taking into account the total power of the consumers in the power network, was modeled by installing solar panels in apartments. As a result, as a result of the use of solar panels with a total power of 34 kW in the houses in the cable distribution network with a length of 0.444 km, a reduction of active power loss of 21382 kWh (37%) is achieved, a reduction of reactive power loss is achieved by 7309kVAR*hour (37%), the level of overloading of the cable network was 1- in the section of households, from 105% to 85%, and in the section of the last households, a decrease is achieved from 14% to 6%. In addition, it was found that the voltage drop at the ends of the apartments is not at the required level, and when the solar panels are connected, it is at the limit values.

ADMM-Based Multi-Objective Control Scheme for Mitigating the Impact of High Penetration DER Integration in the Modern Distribution Systems

The high penetration of renewable energy sources in modern distribution networks poses challenges for grid voltage regulation. In this study, a multi-agent distributed voltage control strategy based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM) is proposed for distribution power systems with a high penetration of photovoltaic (PV) resources coordinated with battery energy storage systems (BESS). In this context, all active and reactive power outputs of the PV are locally optimized through smart inverters to improve the grid voltage with minimum power loss. Uncertainties associated with the solar energy generation and load demands are considered in the defined scenarios. This study consists of two phases. In the first phase, the voltage control problem is formulated as an optimization problem to regulate the voltages within an acceptable limit with fast convergence. In the second phase, a coordinated voltage control strategy for smart PV inverters and BESS is proposed to allocate the power capacity of the battery energy storage systems and the active power loss reduction. Finally, the proposed method is tested on modified IEEE 13-bus, 33-bus and 141-bus distribution systems using MATLAB/Simulink and MATPOWER. A comparison of the results of the voltage profiles with and without the control algorithm demonstrated the efficacy, robustness, and scalability of the distributed scheme for voltage improvement and optimal utilization of PV power under different scenarios.

Optimal Deployment of Unified Power Flow Controller on Nigerian 330 kV Grid Using Artificial Bee Colony

Transmission line power loss minimization and voltage profile improvement are challenging problems in modern power systems due to continuous increase in load demand. In this paper, an optimal location and sizing of unified power flow controller (UPFC) to improve the performance of the updated 54-bus 330 kV Nigerian network was presented. Here, the maximum power loss bus was identified to be the most favorable location for fixing the UPFC under different loading conditions; slightly underloaded (90%), normal loading (100%) and slightly over loaded (110%). The optimum location was determined using the artificial bee colony (ABC) algorithm. The work was implemented in MATLAB® 2021b environment and its performance was evaluated based on the simulation results obtained. The simulation results revealed that, ABC proved to be robust based on the facts that all the voltage profile that violate ± 5% tolerance margin of the nominal voltage criteria were kept within the acceptable limit after the UPFC optimal deployment. Moreover, the proposed approach also recorded significant reduction in the overall active and reactive power loss in the entire network with a minimum percentage average active power loss reduction of 45.3% under different loading conditions.

Reduction of Active and Reactive Power Losses on Transmission Lines using SSSC

This paper discusses a comprehensive study on reduction of power (active and reactive) losses in transmission lines using a second generation FACTS device, Static Synchronons Series Compensator (SSSC). Modern restructured power systems sometimes operate with heavily loaded lines resulting in power losses and higher voltage deviations, which may lead to mal-operation of power system and eventual collapse of the system. This is mainly due to continuous and uncertain growth in demand for electrical power. The paper presents a methodology to solve the problem of power losses in the Nigerian 28 – bus power system by incorporating Static Synchronons Series Compensator in the network using Newton-Rahson power flow algorithm. Simulation of power flow solution without and with the FACTS device was done using a Matlab software. The results showed that the maximum power (active and reactive) loss in the system without SSSC occurred in the transmission line connecting bus 17 (Jebba) to bus 23(Shiroro) and a 19.32% loss reduction was obtained on the line after the incorporation of the SSSC FACTS device giving a power saving of 80.68%. The total system active and reactive power losses before the application of SSSC was 205.183MW and 1594.683MVAR respectively. However, when the FACTS device was applied at the weak buses the total system active and reactive power losses reduced to 144.571MW and 1136.863MVAR respectively giving a percentage loss reduction in active and reactive power of 29.54% and 28.71% respectively resulting in a power saving of 70.46% and 71.29%. Hence, more power was available in the network when compared to the base case due to the installation of SSSC. Also an improvement in the voltage magnitude at the weak buses and other buses were noticed as they were all maintained at 1.0 PU.

Reactive power optimization for distribution network system with wind power based on improved multi-objective particle swarm optimization algorithm

• In the multi-objective particle swarm optimization algorithm, the adaptive mesh is introduced to reflect the density of particles, and according to the density information, the global optimal particles are selected by roulette mechanism and the scale of external repository is maintained, which effectively ensures the uniformity and diversity of Pareto frontier distribution. • The proposed IMOPSOA has faster convergence speed and shorter average computing time than NSGA-II algorithm, can get Pareto frontier with better distribution and better results, which makes the voltage stability of the distribution network system with wind power higher. Aiming at the uncertainty of the grid-connected output of wind turbines, a scenario analysis method based on probability occurrence is used to transform the uncertainty model into multi scenario problems with different occurrence probabilities, a reactive power optimization model is established with the goal of minimizing the active power network loss and voltage deviation. Aiming at the poor diversity of Pareto frontiers obtained by traditional methods, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm uses adaptive grids to obtain the density of particles in external archives, selects the global optimal particle and maintains the scale of the external repository according to the density information using a roulette mechanism, effectively ensuring the uniformity and diversity of the Pareto frontier distribution. The algorithm is used to calculate reactive power optimization of the IEEE 33-bus system with wind power, and compared with the existing NSGA-Ⅱ algorithm. The results show that the Pareto frontier obtained by the proposed algorithm is better, the voltage stability and active power loss reduction rate of the distribution network system with wind power is higher.

Effect of zero bus load flow on network reconfiguration of distribution network

ABSTRACT In this paper, a novel Student Psychology Based Optimization (SPBO) method with Zero Bus Load Flow (ZBLF) is proposed for the optimum placement of distributed generation sources (DGs) in the 33 bus distribution network (DN) with and without the network reconfiguration. To reduce the stress on the utility, a predefined/constant energy is considered to be imported from the utility at different load levels (annual hourly load patterns). The proposed method is used in the presence of multi-objective functions for the optimum placement of DGs with and without network reconfiguration that shows a considerable reduction in active power loss and voltage deviation as well as an increment in the annual energy savings. Besides, the comparative statistical analysis shows that the proposed method scored the first rank for all the cases and all the load percentages. This confirms the superiority of the proposed method over the different existing methods available in the literature.

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.

Grey wolf optimisation algorithm for solving distribution network reconfiguration considering distributed generators simultaneously

ABSTRACT This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is utilised to minimise the active power losses (APL) and enhance the voltage profile. Various scenarios were considered in this context to compare the performance of the proposed algorithm under voltage and current capacity constraints. Furthermore, a detailed validation via comparison of the results is being carried out with other methods from the exhaustive literature. The proposed algorithm reduces the APL by 63.13%, 56.19%, and 34.27% with DNR in IEEE 33, 69 and 118-bus systems. Similarly, APL reduction by 69.61%, 82.09%, and 36.08% with DNR considering DGs simultaneously. The results show that the proposed algorithm is an effective and promising method to solve problems similar to this work.